{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c9e2048",
   "metadata": {},
   "source": [
    "# Fine-Tuning ChatGPT for Different Data Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e8ddc61",
   "metadata": {},
   "source": [
    "# Imports\n",
    "Here we import all the necessary packages and modules for our fine-tuning process.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b410a579",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import pprint\n",
    "import time\n",
    "import argparse\n",
    "from ast import literal_eval\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tqdm import tqdm\n",
    "\n",
    "import wandb\n",
    "from openai import OpenAI\n",
    "import pandas as pd\n",
    "\n",
    "from utils_2 import (\n",
    "    dataset_has_format_errors,\n",
    "    check_token_statistics_and_cost_estimate,\n",
    "    load_dataset_task_prompt_mappings,\n",
    "    write_jsonl,\n",
    ")\n",
    "from utils_1 import task_num_to_task_name, dataset_num_to_dataset_name, plot_count_and_normalized_confusion_matrix, task_to_display_labels\n",
    "\n",
    "# Get the notebook's full path\n",
    "notebook_path = os.getcwd()\n",
    "\n",
    "# Set module_dir to the notebook's directory\n",
    "module_dir = notebook_path\n",
    "\n",
    "api_key_project = \"\" #MK's Key\n",
    "#api_key_project = \"\" #new PRODIGI key\n",
    "\n",
    "client = OpenAI(\n",
    "    api_key=api_key_project,  # This is the default and can be omitted\n",
    ")\n",
    "\n",
    "\n",
    "WANDB_PROJECT_NAME = \"chatgpt_annotations_llm_comparison\"\n",
    "MODEL_NAME = 'gpt-4o-mini-2024-07-18'\n",
    "COMPLETION_RETRIES = 50\n",
    "\n",
    "print(notebook_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772bcc76",
   "metadata": {},
   "source": [
    "# Utility Functions\n",
    "These functions provide various utilities for tasks such as data loading, token statistics, and other preprocessing steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa7e1cb4",
   "metadata": {},
   "source": [
    "## Configuration Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c79142f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuration Variables\n",
    "# Manually set the variables that were previously handled by argparse\n",
    "\n",
    "# Type of task to run inference on\n",
    "task = 1  # Choices: [1]\n",
    "\n",
    "# Dataset to run inference on\n",
    "dataset = 4  # Choices: [1, 2, 3, 4, 5, 6]\n",
    "\n",
    "# Expert Dataset for Second Evaluation\n",
    "dataset_eval = 'x' # Choises: ['x']\n",
    "\n",
    "# Size of the sample to generate\n",
    "sample_size = '100'  # Choices: ['100','200']\n",
    "\n",
    "# Path to the directory to store the generated samples\n",
    "output_dir = '../../annotations/chatGPT/output_fine_tuning/'\n",
    "\n",
    "# Random seed to use\n",
    "seed = 2019\n",
    "\n",
    "# Path to the directory containing the datasets\n",
    "data_dir = '../../annotations/chatGPT/input_fine_tuning/'\n",
    "\n",
    "# Whether to use the full label\n",
    "not_use_full_labels = False\n",
    "\n",
    "# Path to the dataset-task mappings file\n",
    "dataset_task_mappings_fp = os.path.normpath(os.path.join(module_dir, '../../task_mappings', 'dataset_task_mappings.csv'))\n",
    "\n",
    "# Whether to rewrite the dataframe in OpenAI format\n",
    "rewrite_df_in_openai = True\n",
    "\n",
    "# Number of epochs to train the model\n",
    "n_epochs = 5\n",
    "\n",
    "# Batch Size for learning\n",
    "n_batch = 20\n",
    "\n",
    "# Name of the run\n",
    "run_name = 'finetune_chetGPT_hatespeech_citi'\n",
    "\n",
    "# Temperature to use when generating text\n",
    "temp = 0.0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e81ba8",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ef6fa17",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_label_to_completion(label: str, task_num: int, full_label: bool = True) -> str:\n",
    "    new_label = ''\n",
    "\n",
    "    if task_num == 1:\n",
    "        if full_label:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'HATE SPEECH'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'TOXIC SPEECH'\n",
    "            else:\n",
    "                new_label = 'KEINE HATE SPEECH'\n",
    "            assert new_label in ['HATE SPEECH', 'TOXIC SPEECH', 'KEINE HATE SPEECH']\n",
    "        else:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'A'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'C'\n",
    "            else:\n",
    "                new_label = 'B'\n",
    "            assert new_label in ['A', 'B', 'C']\n",
    "\n",
    "    return new_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02978fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_train_and_eval_sets(data_dir: str, dataset_num: int, task_num: int, sample_size: int, dataset_eval:str) \\\n",
    "        -> dict[str, pd.DataFrame]:\n",
    "    datasets = dict()\n",
    "\n",
    "    train_dataset_task_files = glob.glob(os.path.join(data_dir, f'ds_{dataset_num}__task_{task_num}_train_set*.csv'))\n",
    "    eval_set_name = f'ds_{dataset_num}__task_{task_num}_eval_set'\n",
    "    datasets[eval_set_name] = pd.read_csv(os.path.join(data_dir, eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    # Load the additional evaluation dataset specified by dataset_eval\n",
    "    second_eval_set_name = f'ds_{dataset_eval}__task_{task_num}_full_eval'\n",
    "    datasets[second_eval_set_name] = pd.read_csv(os.path.join(data_dir, second_eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    if sample_size == 'all':\n",
    "        train_dfs_ = {fn.strip('.csv'): pd.read_csv(fn, encoding='utf-8') for fn in train_dataset_task_files}\n",
    "        datasets.update(train_dfs_)\n",
    "    else:\n",
    "        train_df_fn = f'ds_{dataset_num}__task_{task_num}_train_set_{sample_size}'\n",
    "        datasets[train_df_fn] = pd.read_csv(os.path.join(data_dir, train_df_fn + '.csv'), encoding='utf-8')\n",
    "\n",
    "        if train_df_fn not in [os.path.basename(fn).strip('.csv') for fn in train_dataset_task_files]:\n",
    "            raise ValueError(f\"Sample size {sample_size} not found for\"\n",
    "                             f\" dataset {dataset_num} and task {task_num}\")\n",
    "\n",
    "    return datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "904e16c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_training_example(system_prompt, user_prompt_format, user_prompt_text, completion):\n",
    "    return {'messages': [\n",
    "        {'role': 'system',\n",
    "         'content': system_prompt},\n",
    "\n",
    "        {'role': 'user',\n",
    "         'content': user_prompt_format.format(text=user_prompt_text)},\n",
    "\n",
    "        {'role': 'assistant',\n",
    "         'content': completion}\n",
    "    ]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bdf692a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets):\n",
    "    hatespeech_open_ai_metadata = list()\n",
    "\n",
    "    df_id_metadata = pd.DataFrame() if not os.path.exists('hatespeech_open_ai_metadata.csv') \\\n",
    "        else pd.read_csv('hatespeech_open_ai_metadata.csv')\n",
    "\n",
    "    for df_name, df in datasets.items():\n",
    "        df_jsonl_filename = os.path.join(output_dir, 'temp', df_name + '.jsonl')\n",
    "        write_jsonl(data_list=df['openai_instance_format'].tolist(), filename=df_jsonl_filename)\n",
    "\n",
    "        if not_use_full_labels:\n",
    "            df_name += '_single_letter_labels'\n",
    "\n",
    "        if (not rewrite_df_in_openai and\n",
    "                (len(df_id_metadata) > 0 and df_name in df_id_metadata['df_name'].tolist())):\n",
    "            print(f\"Dataset {df_name} already uploaded to OpenAI\")\n",
    "            continue\n",
    "\n",
    "        print(f\"Uploading {df_name} to OpenAI\")\n",
    "        df_response = client.files.create(\n",
    "            file=open(df_jsonl_filename, \"rb\"), purpose=\"fine-tune\"\n",
    "        )\n",
    "        df_response_dict = df_response.to_dict()\n",
    "        df_file_id = df_response_dict[\"id\"]\n",
    "\n",
    "        # Wait until the file is processed\n",
    "        while True:\n",
    "            file = client.files.retrieve(df_file_id)\n",
    "            file_dict = file.to_dict()\n",
    "            if file_dict[\"status\"] == \"processed\":\n",
    "                break\n",
    "            time.sleep(15)\n",
    "        hatespeech_open_ai_metadata.append({'df_name': df_name, 'file_id': df_file_id})\n",
    "\n",
    "    df_id_metadata = pd.concat([df_id_metadata, pd.DataFrame(hatespeech_open_ai_metadata)])\n",
    "    df_id_metadata.to_csv('hatespeech_open_ai_metadata.csv', index=False)\n",
    "\n",
    "    return df_id_metadata\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ace921b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name, n_epochs):\n",
    "    response = client.fine_tuning.jobs.create(\n",
    "        training_file=training_file_id,\n",
    "        validation_file=evaluation_file_id,\n",
    "        model=\"gpt-4o-mini-2024-07-18\",\n",
    "        suffix=model_name,\n",
    "        hyperparameters={\"n_epochs\": n_epochs,\n",
    "                         \"batch_size\": n_batch}\n",
    "    )\n",
    "\n",
    "    response_dict = response.to_dict()\n",
    "    job_id = response_dict[\"id\"]\n",
    "    print(\"Job ID:\", response_dict[\"id\"])\n",
    "    print(\"Status:\", response_dict[\"status\"])\n",
    "\n",
    "    # Wait until the job is done\n",
    "    while True:\n",
    "        job = client.fine_tuning.jobs.retrieve(job_id)\n",
    "        job_dict = job.to_dict()\n",
    "        if job_dict[\"status\"] == \"succeeded\":\n",
    "            break\n",
    "        elif job_dict[\"status\"] == \"failed\":\n",
    "            raise Exception(\"Training failed: %s\" % job_dict[\"error\"])\n",
    "        time.sleep(30)\n",
    "\n",
    "    return job_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2026f811",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_and_log_finetuning_event_history(job_id):\n",
    "    response = client.fine_tuning.jobs.list_events(fine_tuning_job_id=job_id)\n",
    "    response_dict = response.to_dict()\n",
    "    events = response_dict[\"data\"]\n",
    "    events.reverse()\n",
    "    for event in events:\n",
    "        print(event[\"message\"])\n",
    "\n",
    "    # Log events\n",
    "    for event in events:\n",
    "        if event['type'] != 'metrics':\n",
    "            continue\n",
    "        data = event['data']\n",
    "        wandb.log(\n",
    "            {\n",
    "                \"train_loss\": data.get(\"train_loss\"),\n",
    "                \"valid_loss\": data.get(\"valid_loss\"),\n",
    "                \"train_mean_token_accuracy\": data.get(\"train_mean_token_accuracy\"),\n",
    "                \"valid_mean_token_accuracy\": data.get(\"valid_mean_token_accuracy\")\n",
    "            },\n",
    "            step=data.get('step', 0)\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc50a97",
   "metadata": {},
   "source": [
    "# Main Implementation\n",
    "This section contains the main code for fine-tuning the ChatGPT model. It includes setup, data preparation, training, and evaluation steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1b7246",
   "metadata": {},
   "source": [
    "### Connect to WandB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "37564c84",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mkublima\u001b[0m (\u001b[33mdigdemlab\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "wandb version 0.19.7 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "Tracking run with wandb version 0.15.11"
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      ]
     },
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     "output_type": "display_data"
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     "data": {
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       "Run data is saved locally in <code>e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\\wandb\\run-20250228_100332-7e69m0ec</code>"
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     "data": {
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       "Syncing run <strong><a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/7e69m0ec' target=\"_blank\">finetune_chetGPT_hatespeech_citi</a></strong> to <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison</a>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       " View run at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/7e69m0ec' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/7e69m0ec</a>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/7e69m0ec?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
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       "<wandb.sdk.wandb_run.Run at 0x1b2d11e37c0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 1/2\n",
    "# Initialize the Weights and Biases run\n",
    "wandb.init(\n",
    "    # set the wandb project where this run will be logged\n",
    "    project=WANDB_PROJECT_NAME,\n",
    "    name=run_name if run_name != '' else f'{MODEL_NAME}_ds_{dataset}_task_{int(task)}'\n",
    "                                                    f'_sample_{sample_size}_epochs_{n_epochs}'\n",
    "                                                    f'_full_label_names_{str(not not_use_full_labels)}'\n",
    "                                                    f'_temp_{temp}',\n",
    "\n",
    "    # track hyperparameters and run metadata\n",
    "    config = {\n",
    "        \"model\": MODEL_NAME,\n",
    "        \"dataset\": dataset_num_to_dataset_name[int(dataset)],\n",
    "        \"task\": task_num_to_task_name[int(task)],\n",
    "        \"epochs\": n_epochs,\n",
    "        \"temp\": temp\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f1df25",
   "metadata": {},
   "source": [
    "### Load and Process Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2454b3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 3\n",
    "# Load the dataset and filename\n",
    "dataset_idx, dataset_task_mappings = load_dataset_task_prompt_mappings(\n",
    "    dataset_num=dataset, task_num=task, dataset_task_mappings_fp=dataset_task_mappings_fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d35437fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 5\n",
    "# Load the train and eval datasets\n",
    "datasets = load_train_and_eval_sets(\n",
    "    data_dir=data_dir, dataset_num=dataset, task_num=task, sample_size=sample_size, dataset_eval=dataset_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f379906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact prompts>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 4\n",
    "# Get information specific to the dataset\n",
    "label_column = dataset_task_mappings.loc[dataset_idx, \"label_column\"]\n",
    "system_prompt = dataset_task_mappings.loc[dataset_idx, 'zero_shot_prompt']\n",
    "user_prompt_format = dataset_task_mappings.loc[dataset_idx, 'user_prompt']\n",
    "    \n",
    "# Log the system prompt and user_prompt_format as files in wandb\n",
    "prompts_artifact = wandb.Artifact('prompts', type='prompts')\n",
    "with prompts_artifact.new_file('system_prompt.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(system_prompt)\n",
    "with prompts_artifact.new_file('user_prompt_format.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(user_prompt_format)\n",
    "wandb.run.log_artifact(prompts_artifact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "47917349",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 6\n",
    "# Generate the training and evaluation examples in the way expected by the Open AI API to finetune chatgpt3.5\n",
    "preprocessed_output_dir = os.path.join(\n",
    "    output_dir, 'preprocessed', 'full_name_labels' if not not_use_full_labels else 'single_letter_labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5a9616d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 7\n",
    "for df_name, df in datasets.items():\n",
    "    df['completion_label'] = df[label_column].map(\n",
    "        lambda label: map_label_to_completion(label=label, task_num = task,\n",
    "                                              full_label=not not_use_full_labels)\n",
    "        )\n",
    "    df['openai_instance_format'] = df.apply(\n",
    "        lambda row: create_training_example(\n",
    "            system_prompt=system_prompt, user_prompt_format=user_prompt_format,\n",
    "            user_prompt_text=row['text'],\n",
    "            completion=row['completion_label']\n",
    "        ),\n",
    "        axis=1\n",
    "    )\n",
    "    df['openai_instance_without_completion'] = df['openai_instance_format'].map(lambda x: x['messages'][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8bcd8c12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check for errors ds_4__task_1_train_set_100 set: \n",
      "No errors found\n"
     ]
    }
   ],
   "source": [
    "# Section 8\n",
    "print(f'Check for errors {df_name} set: ')\n",
    "assert not dataset_has_format_errors(df['openai_instance_format'].tolist()), f\"Errors found in {df_name}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b1d7833",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 9\n",
    "df.to_csv(os.path.join(preprocessed_output_dir, df_name + '.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24cdc6b4",
   "metadata": {},
   "source": [
    "### Upload Training Dataset to OPEN AI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2fd9b187",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading ds_4__task_1_eval_set to OpenAI\n",
      "Uploading ds_x__task_1_full_eval to OpenAI\n",
      "Uploading ds_4__task_1_train_set_100 to OpenAI\n"
     ]
    }
   ],
   "source": [
    "# Section 10\n",
    "# Create jsonl file and upload to OpenAI\n",
    "df_id_metadata =upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "705758da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 11\n",
    "# delete all files in the temp folder\n",
    "os.system(f\"rm -rf {os.path.join(output_dir, 'temp')}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4519ab5a",
   "metadata": {},
   "source": [
    "### Finetune chatGPT with the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0ec6e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 12\n",
    "# Run training on the train_df samples selected\n",
    "eval_set_name = f'ds_{dataset}__task_{task}_eval_set'\n",
    "eval_df = datasets[eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a76b1ab7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finetuning ds_4__task_1_train_set_100\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Section 13\n",
    "print(f\"Finetuning {df_name}\")\n",
    "print('-' * 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a1036bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 14\n",
    "# Log in wandb.config the dataset sample size used\n",
    "sample_size = df_name.split('_')[-1]\n",
    "wandb.config['trainset_size'] = sample_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d0860e65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the evaluation set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 847, 999\n",
      "mean / median: 879.8375634517766, 869.0\n",
      "p5 / p95: 853.0, 920.0\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 5.241116751269035, 6.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~346656 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~1733280 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 15\n",
    "# Check statistics and possible cost for each dataset\n",
    "print('Statistics for the evaluation set: ')\n",
    "eval_tokens = check_token_statistics_and_cost_estimate(\n",
    "    datasets[eval_set_name]['openai_instance_format'].tolist(), target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "254bdfe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the training set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 848, 997\n",
      "mean / median: 879.23, 867.5\n",
      "p5 / p95: 854.0, 918.0\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 4.99, 5.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~87923 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~439615 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 16\n",
    "print(f'Statistics for the training set: ')\n",
    "train_tokens = check_token_statistics_and_cost_estimate(df['openai_instance_format'].tolist(),\n",
    "                                                        target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "91aed7d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of tokens: 434579\n",
      "\n",
      "#### Estimated cost: 3.48 USD\n"
     ]
    }
   ],
   "source": [
    "# Section 17\n",
    "print(f\"Total number of tokens: {eval_tokens + train_tokens}\\n\")\n",
    "print(f\"#### Estimated cost: {round((eval_tokens + train_tokens) * (0.008 / 1000), 2)} USD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5a4cb776",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 18\n",
    "# Run the fine-tuning job\n",
    "training_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == df_name, 'file_id'].values[0]\n",
    "evaluation_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == eval_set_name, 'file_id'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6b0baf56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job ID: ftjob-RG8Reb9fbNufyFWVQ7cPCcUE\n",
      "Status: validating_files\n"
     ]
    }
   ],
   "source": [
    "# Section 19\n",
    "model_name = (df_name.replace('__', '_')\n",
    "              .replace('train_set', 'trn')\n",
    "              .replace('task', 't')\n",
    "              .replace('_single_letter_labels', '_sl'))\n",
    "job_id = fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name=model_name, n_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6aff8caf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model ft:gpt-4o-mini-2024-07-18:university-of-zurich-ipz-phd:ds-4-t-1-trn-100:B5qiJdL8 has been successfully fine-tuned\n"
     ]
    }
   ],
   "source": [
    "# Section 20\n",
    "# Print the model name\n",
    "response = client.fine_tuning.jobs.retrieve(job_id)\n",
    "response_dict = response.to_dict()\n",
    "full_model_name = response_dict[\"fine_tuned_model\"]\n",
    "print(f'The model {full_model_name} has been successfully fine-tuned')\n",
    "wandb.config['model_name_openai'] = full_model_name\n",
    "wandb.config['finetuning_jobid'] = job_id\n",
    "wandb.config['training_file_openai_id'] = response_dict['training_file']\n",
    "wandb.config['validation_file_openai_id'] = response_dict['validation_file']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "45b169bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 10/25: training loss=0.11, validation loss=0.10, full validation loss=0.10\n",
      "Step 11/25: training loss=0.12, validation loss=0.11\n",
      "Step 12/25: training loss=0.10, validation loss=0.10\n",
      "Step 13/25: training loss=0.08, validation loss=0.06\n",
      "Step 14/25: training loss=0.09, validation loss=0.12\n",
      "Step 15/25: training loss=0.08, validation loss=0.21, full validation loss=0.13\n",
      "Step 16/25: training loss=0.09, validation loss=0.12\n",
      "Step 17/25: training loss=0.08, validation loss=0.10\n",
      "Step 18/25: training loss=0.09, validation loss=0.22\n",
      "Step 19/25: training loss=0.07, validation loss=0.05\n",
      "Step 20/25: training loss=0.05, validation loss=0.10, full validation loss=0.10\n",
      "Step 21/25: training loss=0.03, validation loss=0.10\n",
      "Step 22/25: training loss=0.03, validation loss=0.07\n",
      "Step 23/25: training loss=0.11, validation loss=0.11\n",
      "Step 24/25: training loss=0.04, validation loss=0.05\n",
      "Step 25/25: training loss=0.05, validation loss=0.10, full validation loss=0.10\n",
      "Checkpoint created at step 15\n",
      "Checkpoint created at step 20\n",
      "New fine-tuned model created\n",
      "The job has successfully completed\n"
     ]
    }
   ],
   "source": [
    "# Section 21\n",
    "# Print the events (training history of the model)\n",
    "print_and_log_finetuning_event_history(job_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c579b37",
   "metadata": {},
   "source": [
    "## Evaluate Model on Validation Set with Annotations of the Group from which the Training Data is!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fba537b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Section 22\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0a544a5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 394/394 [04:16<00:00,  1.54it/s]\n"
     ]
    }
   ],
   "source": [
    "# Section 23\n",
    "for messages in tqdm(eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "337b13ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 24\n",
    "# Add predictions to df\n",
    "eval_df['prediction'] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fe242636",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 25\n",
    "# Store output\n",
    "predictions_output_dir = os.path.join(output_dir, 'predictions',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir, exist_ok=True)\n",
    "datasets[eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bcaddde5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 26\n",
    "# Get performance metrics\n",
    "y_true = eval_df['completion_label']\n",
    "y_pred = eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3d085eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 27\n",
    "label_type = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels = task_to_display_labels[task][label_type]\n",
    "labels = display_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a1ce1492",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.6384180790960452,\n",
      "                 'precision': 0.5231481481481481,\n",
      "                 'recall': 0.8188405797101449,\n",
      "                 'support': 138},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.7064676616915423,\n",
      "                       'precision': 0.8402366863905325,\n",
      "                       'recall': 0.6094420600858369,\n",
      "                       'support': 233},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.18750000000000003,\n",
      "                  'precision': 0.3333333333333333,\n",
      "                  'recall': 0.13043478260869565,\n",
      "                  'support': 23},\n",
      " 'accuracy': 0.6548223350253807,\n",
      " 'macro avg': {'f1-score': 0.5107952469291958,\n",
      "               'precision': 0.5655727226240047,\n",
      "               'recall': 0.5195724741348925,\n",
      "               'support': 394},\n",
      " 'weighted avg': {'f1-score': 0.6523379697700091,\n",
      "                  'precision': 0.6995844138073736,\n",
      "                  'recall': 0.6548223350253807,\n",
      "                  'support': 394}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2796NoqIi7Tw+FxcXyGQyKJVKjB49GmFhYXB1dYWLiwsmT56MJk2aaFf/GovJXznY29ujfv36OmWP32Tx8uXLOHXqFE6fPo2pU6dqy4uKirB582a8/fbbRp+vbt26JiWLAFCvXj3Uq1cPY8aMQUREBPz8/LB161aMGjUKQPF8vrNnz0IqlcLNzQ329vrzSapXr673Ph/n6elZah0/Pz9cuXLFqHiNuaaPmz59Ot5//33ta7VarTf3gnQJADSWG9mnCiHg7ZCzCAq6janhnfDPPw56NUoSP49amZg2tRMyM82bXE7PPhuZUNkhkJni4+N1pn2V/P0aMWIEIiMjsXPnTgDQWwh56NAhdOzYEQCwaNEiWFtb47XXXkNOTg66dOmCuLg4baeLsZj8VZDY2Fi0b98ey5cv1ynfuHEjYmNjTUr+zOXt7Q07OztkZWVpy6RS6RMTO3MNGTIEM2bMwLlz5/Tm/RUWFiIvL89g0mksS6yoelYVZQN5N/99nX8byE4ArJ0AmTtQmAHkpwCFqcX785KK/2vjCthUB/JuAel7Ace2gLUzUJAK/BMHSOVlryKmyjdhwhl07HQDUbPbISfHGs7OOQCArCwb5OdbQyrVIOKDn1G/fhpmzfwPpFJBWyczU4bCQtP+CNCzZdS0ZJw+6Ii7d2SwdShCx37paBr0EB8M9ans0J4rRZCgyMSbNBtqwxQdO3aEIJSexJe1r4RCocCyZcuwbNkyk879OCZ/FaCgoAAbN25EVFSU3qKLMWPGYP78+bhw4QKaNWtmVHupqanIzdWd7+Hq6mpwwURkZCSys7Px0ksvwcvLC+np6Vi6dCkKCgrQrVs3k95HZmam3vJxOzs7nQmo9+/f16tTrVo1KBQKhIaG4vvvv0eXLl3w0UcfoV27dnB0dER8fDzmzZuH2NhY3uqlFNmXgb8fucvOnU+L/+vcB/CKAjKOADdn/bv/+v/uB+g2DnAfD0hlwMNzwN1NQJEasHYFHF4AfOMAG5en9jaoHHr3+RsAMP+TQzrln376Ig7sr4vqNXLQtm3xPX5WrNynUyc8vBN+/83wbaCoaqhWoxBTlt2AS81CZGdaIfGKAh8M9cHZo45PPpiMVhnDvs8SJn8VYOfOnbh//z7+7//+T2+fr68vmjRpgtjYWCxdutSo9gwt8Pjll1/Qpk0bvfIOHTpg+fLlGD58OP755x84OzujRYsW2LdvX5kLRQyZOXMmZs6cqVM2btw4rFq1Svva0DyDzZs3Y9CgQZDL5di/fz8WLVqE1atXY/LkybCzs0PDhg0xadIkk1Yji41jK6D5udL3u/Yt3kpjUxOop/8EIaoCevV8vcz9qf/YP7EOVV2Lwjh1hSqeRDCmn5GoClCr1VAqlZh4vB/kDpz8/rz7bYRxK8np+aD57Y/KDoGegkKhAIexAxkZGXoPN7CEkr8TM091hcLMvxO5DwsQ1fpAhcVakdjzR0RERKLCYV8iIiIiESkSpCgyM3kz9/jKVHUjJyIiIiKTseePiIiIREWABBozb/UimHl8ZWLyR0RERKLCYV8iIiIiEg32/BEREZGoaAQJNIJ5w7bmHl+ZmPwRERGRqBRBiiIzBz/NPb4yVd3IiYiIiMhk7PkjIiIiUeGwLxEREZGIaCCFxszBT3OPr0xVN3IiIiIiMhl7/oiIiEhUigQJiswctjX3+MrE5I+IiIhEhXP+iIiIiEREEKTQmPmEDoFP+CAiIiKiqoA9f0RERCQqRZCgCGbO+TPz+MrE5I+IiIhERSOYP2dPI1gomErAYV8iIiIiEWHPHxEREYmKxgILPsw9vjIx+SMiIiJR0UACjZlz9sw9vjJV3bSViIiIiEzGnj8iIiISFT7hg4iIiEhExD7nr+pGTkREREQmY88fERERiYoGFni2bxVe8MHkj4iIiERFsMBqX4HJHxEREVHVoBEs0PNXhRd8cM4fERERkYiw54+IiIhEReyrfZn8ERERkahw2JeIiIiIRIM9f0RERCQqYn+2L5M/IiIiEhUO+xIRERGRaLDnj4iIiERF7D1/TP6IiIhIVMSe/HHYl4iIiEhE2PNHREREosKePyIiIiIREfDv7V7KuwkmnvPo0aPo06cPPDw8IJFI8N133+nGJAiIjIyEh4cHbG1t0bFjR1y6dEmnTl5eHiZOnIjq1avD3t4effv2xa1bt0x+/0z+iIiISFRKev7M3UyRlZWFZs2a4bPPPjO4f/78+Vi4cCE+++wznD59GiqVCt26dUNmZqa2TmhoKL799lts2bIFx48fx8OHD9G7d28UFRWZFAuHfYmIiIgqWK9evdCrVy+D+wRBwOLFixEREYFXXnkFALB+/Xq4ublh06ZNGDduHDIyMhAbG4uNGzeia9euAIAvv/wSnp6eOHDgAHr06GF0LOz5IyIiIlGpjJ6/siQmJiIlJQXdu3fXlsnlcnTo0AEnTpwAAJw5cwYFBQU6dTw8PNC4cWNtHWOx54+IiIhExZILPtRqtU65XC6HXC43qa2UlBQAgJubm065m5sbrl+/rq0jk8ng7OysV6fkeGOx54+IiIionDw9PaFUKrVbTExMuduSSHQTUkEQ9MoeZ0ydx7Hnj4iIiETFkj1/N2/ehJOTk7bc1F4/AFCpVACKe/fc3d215ampqdreQJVKhfz8fKSlpen0/qWmpiIoKMik87Hnj4iIiERFECQW2QDAyclJZytP8le3bl2oVCrs379fW5afn48jR45oE7uWLVvCxsZGp05ycjIuXrxocvLHnj8iIiKiCvbw4UP89ddf2teJiYk4f/48XFxcUKdOHYSGhiI6Ohq+vr7w9fVFdHQ07OzsMGTIEACAUqnE6NGjERYWBldXV7i4uGDy5Mlo0qSJdvWvsZj8ERERkaiU3KjZ3DZMER8fj06dOmlfv//++wCAESNGIC4uDuHh4cjJyUFISAjS0tLQunVr7Nu3D46OjtpjFi1aBGtra7z22mvIyclBly5dEBcXBysrK5NikQiCYOpNqomeSWq1GkqlEhOP94Pcwaayw6EK9tsI/8oOgZ4izW9/VHYI9BQUCgU4jB3IyMjQmUdnKSV/J1p/NwnW9qYPzz6qMCsPp/ovrbBYKxLn/BERERGJCId9iYiISFQeXbBhThtVFZM/IiIiEhVL3uqlKmLyR0RERKIi9p4/zvkjIiIiEhH2/NFz5/u/AiC1U1R2GFTBEvZsqOwQ6Cnq4dG8skOg54hggWHfqtzzx+SPiIiIREUAYO6N7qryffI47EtEREQkIuz5IyIiIlHRQALJU37Cx7OEyR8RERGJClf7EhEREZFosOePiIiIREUjSCDhTZ6JiIiIxEEQLLDatwov9+WwLxEREZGIsOePiIiIREXsCz6Y/BEREZGoMPkjIiIiEhGxL/jgnD8iIiIiEWHPHxEREYmK2Ff7MvkjIiIiUSlO/syd82ehYCoBh32JiIiIRIQ9f0RERCQqXO1LREREJCLC/zZz26iqOOxLREREJCLs+SMiIiJR4bAvERERkZiIfNyXyR8RERGJiwV6/lCFe/4454+IiIhIRNjzR0RERKLCJ3wQERERiYjYF3xw2JeIiIhIRNjzR0REROIiSMxfsFGFe/6Y/BEREZGoiH3OH4d9iYiIiESEPX9EREQkLrzJ85MtXbrU6AYnTZpU7mCIiIiIKprYV/salfwtWrTIqMYkEgmTPyIiIqJnmFHJX2JiYkXHQURERPT0VOFhW3OVe8FHfn4+EhISUFhYaMl4iIiIiCpUybCvuVtVZXLyl52djdGjR8POzg4BAQG4ceMGgOK5fh9//LHFAyQiIiKyKMFCWxVlcvI3ffp0XLhwAYcPH4ZCodCWd+3aFVu3brVocERERERkWSbf6uW7777D1q1b0aZNG0gk/3Z5NmrUCH///bdFgyMiIiKyPMn/NnPbqJpMTv7u3r2LmjVr6pVnZWXpJINEREREzySR3+fP5GHfwMBAfP/999rXJQnfmjVr0LZtW8tFRkRERPScKCwsxAcffIC6devC1tYWPj4+iIqKgkaj0dYRBAGRkZHw8PCAra0tOnbsiEuXLlk8FpN7/mJiYtCzZ09cvnwZhYWFWLJkCS5duoRffvkFR44csXiARERERBZVCT1/8+bNw6pVq7B+/XoEBAQgPj4eo0aNglKpxLvvvgsAmD9/PhYuXIi4uDj4+flhzpw56NatGxISEuDo6GhmwP8yuecvKCgIP//8M7Kzs1GvXj3s27cPbm5u+OWXX9CyZUuLBUZERERUIQSJZTYT/PLLL+jXrx9efvlleHt749VXX0X37t0RHx9fHJIgYPHixYiIiMArr7yCxo0bY/369cjOzsamTZss+vbL9WzfJk2aYP369RYNhIiIiKiqUavVOq/lcjnkcrlevXbt2mHVqlW4evUq/Pz8cOHCBRw/fhyLFy8GUPxAjZSUFHTv3l2nrQ4dOuDEiRMYN26cxWIuV/JXVFSEb7/9FleuXIFEIkHDhg3Rr18/WFuXqzkiIiKip0YQijdz2wAAT09PnfJZs2YhMjJSr/7UqVORkZEBf39/WFlZoaioCHPnzsXgwYMBACkpKQAANzc3nePc3Nxw/fp184J9jMnZ2sWLF9GvXz+kpKSgQYMGAICrV6+iRo0a2LlzJ5o0aWLRAImIiIgsyoJz/m7evAknJydtsaFePwDYunUrvvzyS2zatAkBAQE4f/48QkND4eHhgREjRmjrPX7nFEEQLH43FZOTvzFjxmgnKjo7OwMA0tLSMHLkSLz11lv45ZdfLBogERER0bPKyclJJ/krzZQpUzBt2jQMGjQIQPEUuuvXryMmJgYjRoyASqUCUNwD6O7urj0uNTVVrzfQXCYv+Lhw4QJiYmK0iR8AODs7Y+7cuTh//rwlYyMiIiKyvEpY8JGdnQ2pVDftsrKy0t7qpW7dulCpVNi/f792f35+Po4cOYKgoCDz3/MjTO75a9CgAf755x8EBATolKempqJ+/foWC4yIiIioIkiE4s3cNkzRp08fzJ07F3Xq1EFAQADOnTuHhQsX4s033yxuTyJBaGgooqOj4evrC19fX0RHR8POzg5DhgwxL9jHGJX8PbqSJTo6GpMmTUJkZCTatGkDADh58iSioqIwb948iwZHREREZHGVcJ+/ZcuW4cMPP0RISAhSU1Ph4eGBcePGYebMmdo64eHhyMnJQUhICNLS0tC6dWvs27fPovf4AwCJIDx5vYtUKtWZbFhySEnZo6+LioosGiCRsdRqNZRKJXziZkBqp6jscKiCJfxnQ2WHQE9RD4/mlR0CPQWFQgEOYwcyMjKMmkdnqpK/E56LoyC1Ne/vhCYnFzdDZ1ZYrBXJqJ6/Q4cOVXQcRERERE9HOebsGWyjijIq+evQoUNFx0FERET0dFTCsO+zpNx3Zc7OzsaNGzeQn5+vU960aVOzgyIiIiKiimFy8nf37l2MGjUKP/74o8H9nPNHREREzzSR9/yZfJ+/0NBQpKWl4eTJk7C1tcWePXuwfv16+Pr6YufOnRURIxEREZHlCBbaqiiTe/4OHjyIHTt2IDAwEFKpFF5eXujWrRucnJwQExODl19+uSLiJCIiIiILMLnnLysrCzVr1gQAuLi44O7duwCKH1Ny9uxZy0ZHREREZGmV8ISPZ0m5nvCRkJAAb29vNG/eHKtXr4a3tzdWrVql8yw6IrKgIgHVvv4H9sfTYJVeiCJnGzzs4IyMV2oC0uJfQJLcIjhvSoHdaTWkmYUorCFDZq/qyOzuWsnBU1l+P2mPr1fUxJ+/2+HBPzaYFZuIoF4ZBusuCa+NH76sjnGzb+OVscX/8FanWWHjAhXOHnHE3TsyOLkUIqhnBkaEJ8PeSfM03wpZQO/h9/Dy8Ptw8yxeTHk9QYGvFrkh/lDVuo/cs64ynvDxLCnXnL/k5GQAwKxZs7Bnzx7UqVMHS5cuRXR0tEltjRw5Ev3799cp++abb6BQKDB//nwAQGRkJCQSid7m7++vPaZjx44IDQ3VeS2RSLBlyxadthcvXgxvb2/t67i4OINtKxSl3/jx8OHDkEgkSE9P19vn7e2NxYsX65VHR0fDysoKH3/8sU5dQ+cu2Tp27FhmvUfbety1a9cwePBgeHh4QKFQoHbt2ujXrx+uXr2qrfNoW46OjmjVqhW2b9+u3W/sdTdUZ/z48TrxHDp0CC+99BJcXV1hZ2eHRo0aISwsDLdv3y73NRUb5Y5UOB64jwdv1sKdhQ2QNlQF5a67cNxzX1vHZX0ybM9n4t47nrizsAHUL1eHy7rbsD1tOJGgZ0NuthQ+ATmYMPdWmfVO/KjEH2ft4arSvcPCg39scP8fG4ydeQerDv6ByYtvIP6wIxaG1anIsKmC3E22wdpod0zs5YeJvfxw4WcHRK5LgpdfbmWHRs8Rk3v+hg4dqv3/Fi1aICkpCX/88Qfq1KmD6tWrmxXMF198gQkTJmD58uUYM2aMtjwgIAAHDhzQqWttXXboCoUCH3zwAQYMGAAbG5tS6zk5OSEhIUGn7NGnmVjCunXrEB4ejrVr12LatGkAgNOnT2tXRp84cQIDBgxAQkKC9i7hMplMe3xUVBTGjh2r02Zpj3rJz89Ht27d4O/vj+3bt8Pd3R23bt3CDz/8gIwM3SRg3bp16NmzJ9LT0/HJJ59g4MCBOH78ONq2bQvAuOs+duxYREVF6ZTZ2dlp/3/16tUICQnBiBEjsG3bNnh7e+PGjRvYsGEDPv30UyxcuLDsi0cAAPmf2chu5YScF4p/PgprypDzczrk17KRWVLnahYednBGboADAOBhV1c4HngA+bUc5AQqKylyepLAzpkI7JxZZp17yTZY/kEtzN10DTPf8NHZ5+2fi5lfJGlfe3jnY+TUZMyf6IWiQsCq3Df0ospwar/udzVunjt6D78P/5ZZuH6VTy6yGJGv9jX714KdnR1eeOEFswOZP38+Zs6ciU2bNmHAgAE6+6ytraFSqUxqb/Dgwdi1axfWrFmDkJCQUutJJBKT2zbFkSNHkJOTg6ioKGzYsAFHjx5F+/btUaNGDW0dFxcXAEDNmjVRrVo1vTYcHR2NjvHy5cu4du0aDh48CC8vLwCAl5cXgoOD9epWq1YNKpUKKpUKq1atwpYtW7Bz505t8mfMdbezsyu1zq1btzBp0iRMmjQJixYt0pZ7e3ujffv2Bnv6yLC8BvZwPHAf1nfyUOghh01SDhQJ2Xgw4t+pFnn+9rCLV+NhJxcUOVtDcSkLNsl5eDDSoxIjJ3NpNMD8SXXw6tup8G5gXO9PltoKdg4aJn5VnFQq4D990iG30+BKvH1lh0PPEaN+Nbz//vtGN1ienpxp06Zh+fLl2L17N7p27Wry8YY4OTlhxowZiIqKwogRI2BvXzlfnNjYWAwePBg2NjYYPHgwYmNj0b59+wo7X40aNSCVSvHNN98gNDQUVlZWRh1nY2MDa2trFBQUWCyWr7/+Gvn5+QgPDze431CiS4Zl9KsBSXYRar2fUDxZQwOkv65CVrCzts79UR6ovvo2PN++AsEKgESCe+NqI8+ffzSqsv8urwkrKwH9R98zqr76gRU2LVbhpTeMq0/PHm//HCze9Rdkcg1ysqSIGu2NG3+y18+SJLDAnD+LRFI5jEr+zp07Z1Rj5Rku/fHHH7Fjxw789NNP6Ny5s8E6v//+OxwcHHTKBg0ahC+++KLMtkNCQrBkyRIsXLgQH374ocE6GRkZem0HBQVh3759ZbZdu3ZtvbLs7Gyd12q1Gtu2bcOJEycAAMOGDUNwcDCWLVtm0kOgp06dig8++ECnbPfu3dp5gY+qVasWli5divDwcMyePRutWrVCp06dMHToUPj4+OjVB4C8vDx88sknUKvV6NKli7bcmOu+YsUKvc9h+fLlGDFiBP788084OTkZvRDImGv6eNx5eXna12q12qjzVEX2JzLgcDwd9ybWQb6nHLKkXLisv4NCF2tkdSjuOXb68T7kf2bhn3BvFFa3geJKFlxjb6OomjVymxqeJkDPtj9/s8V3X9TA8r0JMObXa1amFB8O90Edv1wMez+l4gOkCnHrbzlCuvnB3qkI7V7OwOQlNzDllfpMAMlijEr+Dh06VGEBNG3aFPfu3cPMmTMRGBhocC5bgwYN9G4gXdqct0fJ5XJERUXhnXfewdtvv22wjqOjo94tamxtbZ/Y9rFjx/RieDwZ27RpE3x8fNCsWTMAQPPmzeHj44MtW7bgrbfeeuI5SkyZMgUjR47UKatVq1ap9SdMmIDhw4fj0KFDOHXqFL7++mtER0dj586d6Natm7be4MGDYWVlhZycHCiVSixYsAC9evXS7jfmug8dOhQRERE6ZSW3AhIEwaR/EBhzTR8VExOD2bNnG91+Veb8VTIy+tVAVnA1AEBBHVtY381Hte/uIquDCyT5GjhvTkHqZC/tvMACL1vIknKg3H2XyV8V9fspB6Tfs8awwABtmaZIgjWzPfDdmhrY8OtlbXn2QykihtSDwk6DWbGJsC59qjM94woLpLiTJAcA/PmbHRo0z0b/MXexdKpnJUf2HLHErVrEdKsXS6tVqxa2bduGTp06oWfPntizZ49eAiCTyVC/fv1ytT9s2DAsWLAAc+bM0VnpW0IqlZar7bp16+oNWz6+GGLt2rW4dOmSTrlGo0FsbKxJyV/16tVNjtHR0RF9+/ZF3759MWfOHPTo0QNz5szRSf4WLVqErl27wsnJSZuwPcqY665UKkut4+fnh4yMDCQnJxvV+2fMNX3U9OnTdaYkqNVqeHo+n78cJXka6HX9SCWA8L9xi0IBkiJBfxxCKqnSk5LFruuAB3jhP7qLQWYM8UGXAWno/voDbVlWZnHiZyMTMDvuGmQKfujPGxsZP1OLEvmCD5Nv9VIR6tSpgyNHjiA1NRXdu3e36PCdVCpFdHQ0Vq5ciaSkJIu1+yS///474uPjcfjwYZw/f167HT16FKdPn8bFixefWiwlt2jJysrSKVepVKhfv77BxM8SXn31VchkMu1tex5n7oIPuVwOJycnne15ldPSCcpvU2F7Vg3r1HzY/ZoBp+/vIvt/q3gFOyvkNrKH85fJUFx6COvUfDgcfgD7o2naOvRsysmS4u+Ltvj7YvGIQ8pNGf6+aIvUWzZwcimCt3+uzmZtDTjXLIRn/eIpD9kPpZgxuB5ys6V479MbyH5ohQep1niQag0+ar3qGTUtGY1ffAi32vnw9s/ByKnJaBr0EIe+dX7ywURGqvSevxK1a9fG4cOH0alTJ3Tv3h179+6FUln8R6uwsBApKbrzVyQSCdzc3Ixqu3fv3mjdujVWr16td4wgCHptA8VDl1Jp+XPj2NhYvPjiiwYXd7Rt2xaxsbE6K2DLkpmZqRejnZ2dwWTn/PnzmDVrFt544w00atQIMpkMR44cwdq1azF16lST3oMx1z07O1uvjlwuh7OzMzw9PbFo0SK88847UKvVGD58OLy9vXHr1i1s2LABDg4O+PTTT02KSazuj/KA89Z/4Bp7G9KMQhS52CCzqyvSX/03cb/7bh1U25SC6stuQPqwCEU1ZEgfpEJmN5dKjJye5OoFO4S/+m/v+erI4ikd3V57gMmLbzzx+D9/s8MfZ4sX9YwKaqSzb/2py1B55hs6jJ5R1WoUYsqyG3CpWYjsTCskXlHgg6E+OHuUUzcsSuQ9f89M8gcUDwEfOXIEnTp1Qrdu3bSLLi5duqQ3bCiXy5Gba/xNL+fNm4egoCC9crVabXBIMjk5udy3gMnPz8eXX35ZarI1YMAAxMTEYN68eTr38yvNzJkzMXPmTJ2ycePGYdWqVXp1a9euDW9vb8yePRtJSUmQSCTa1++9955J78OY675mzRqsWbNGp06PHj2wZ88eAMWLbvz8/LBgwQL83//9H3JycuDt7Y3evXubtIpc7ARbKzwY6VHmbVuKqtngfsjzOez9PGsW9BB775w3uv6j8/zKczw92xaF8Tv8NIj9CR8SQRCqcPhE/1Kr1VAqlfCJmwGpHVfFPe8S/rOhskOgp6iHR/PKDoGegkKhAIexAxkZGRUylafk74T33LmQlvE0L2NocnORFBFRYbFWpHKNa27cuBHBwcHw8PDA9evXARQ/Om3Hjh0WDY6IiIjI4gQLbVWUycnfypUr8f777+Oll15Cenq69hFl1apV4zNYiYiI6NnH5M80y5Ytw5o1axAREaHz9IhWrVrh999/t2hwRERERGRZJi/4SExMRIsWLfTK5XK53q1EiIiIiJ41Yl/wYXLPX926dXH+/Hm98h9//BGNGjXSP4CIiIjoWVLyhA9ztyrK5J6/KVOmYMKECcjNzYUgCPj111+xefNmxMTEPPFZu0RERESVjvf5M82oUaNQWFiI8PBwZGdnY8iQIahVqxaWLFmCQYMGVUSMRERERGQh5brJ89ixYzF27Fjcu3cPGo2mwh4PRkRERGRpYp/zZ9YTPqpXr26pOIiIiIieDg77mqZu3bqQSEqf5Hjt2jWzAiIiIiKiimNy8hcaGqrzuqCgAOfOncOePXswZcoUS8VFREREVDEsMOwrqp6/d99912D58uXLER8fb3ZARERERBVK5MO+5Xq2ryG9evXCtm3bLNUcEREREVUAsxZ8POqbb76Bi4uLpZojIiIiqhgi7/kzOflr0aKFzoIPQRCQkpKCu3fvYsWKFRYNjoiIiMjSeKsXE/Xv31/ntVQqRY0aNdCxY0f4+/tbKi4iIiIiqgAmJX+FhYXw9vZGjx49oFKpKiomIiIiIqogJi34sLa2xttvv428vLyKioeIiIioYgkW2qook1f7tm7dGufOnauIWIiIiIgqXMmcP3O3qsrkOX8hISEICwvDrVu30LJlS9jb2+vsb9q0qcWCIyIiIiLLMjr5e/PNN7F48WK8/vrrAIBJkyZp90kkEgiCAIlEgqKiIstHSURERGRJVbjnzlxGJ3/r16/Hxx9/jMTExIqMh4iIiKhi8T5/xhGE4nfp5eVVYcEQERERUcUyacHHozd3JiIiIqqKKmvBx+3btzFs2DC4urrCzs4OzZs3x5kzZ7T7BUFAZGQkPDw8YGtri44dO+LSpUsWfOfFTFrw4efn98QE8MGDB2YFRERERFShKmHYNy0tDcHBwejUqRN+/PFH1KxZE3///TeqVaumrTN//nwsXLgQcXFx8PPzw5w5c9CtWzckJCTA0dHRzID/ZVLyN3v2bCiVSoudnIiIiEgM5s2bB09PT6xbt05b5u3trf1/QRCwePFiRERE4JVXXgFQvN7Czc0NmzZtwrhx4ywWi0nJ36BBg1CzZk2LnZyIiIjoabPks33VarVOuVwuh1wu16u/c+dO9OjRAwMHDsSRI0dQq1YthISEYOzYsQCAxMREpKSkoHv37jptdejQASdOnLBo8mf0nD/O9yMiIqLnggWf8OHp6QmlUqndYmJiDJ7y2rVrWLlyJXx9fbF3716MHz8ekyZNwoYNGwAAKSkpAAA3Nzed49zc3LT7LMXk1b5EREREVOzmzZtwcnLSvjbU6wcAGo0GrVq1QnR0NACgRYsWuHTpElauXInhw4dr6z3e2VZyH2VLMrrnT6PRcMiXiIiIqj4L9vw5OTnpbKUlf+7u7mjUqJFOWcOGDXHjxg0AgEqlAgC9Xr7U1FS93kBzmfxsXyIiIqKqrDJu9RIcHIyEhASdsqtXr2rvn1y3bl2oVCrs379fuz8/Px9HjhxBUFCQ2e/5USY/25eIiIioSquEW7289957CAoKQnR0NF577TX8+uuv+Pzzz/H5558DKB7uDQ0NRXR0NHx9feHr64vo6GjY2dlhyJAhZgari8kfERERUQULDAzEt99+i+nTpyMqKgp169bF4sWLMXToUG2d8PBw5OTkICQkBGlpaWjdujX27dtn0Xv8AUz+iIiISGwq6dm+vXv3Ru/evUvdL5FIEBkZicjIyPLHZQQmf0RERCQqlrzPX1XEBR9EREREIsKePyIiIhKXShr2fVYw+SMiIiJR4bAvEREREYkGe/6IiIhIXDjsS0RERCQiIk/+OOxLREREJCLs+SMiIiJRkfxvM7eNqorJHxEREYmLyId9mfwRERGRqPBWL0REREQkGuz5IyIiInHhsC8RERGRyFTh5M1cHPYlIiIiEhH2/BEREZGoiH3BB5M/IiIiEheRz/njsC8RERGRiLDnj4iIiESFw75EREREYsJhXyIiIiISC/b80XPHZ2Y6rKXyyg6DKljP260qOwR6qgorOwB6jnDYl4iIiEhMRD7sy+SPiIiIxEXkyR/n/BERERGJCHv+iIiISFQ454+IiIhITDjsS0RERERiwZ4/IiIiEhWJIEAimNd1Z+7xlYnJHxEREYkLh32JiIiISCzY80dERESiwtW+RERERGLCYV8iIiIiEgv2/BEREZGocNiXiIiISExEPuzL5I+IiIhERew9f5zzR0RERCQi7PkjIiIiceGwLxEREZG4VOVhW3Nx2JeIiIhIRNjzR0REROIiCMWbuW1UUez5IyIiIlEpWe1r7lZeMTExkEgkCA0N1ZYJgoDIyEh4eHjA1tYWHTt2xKVLl8x/swYw+SMiIiJ6Sk6fPo3PP/8cTZs21SmfP38+Fi5ciM8++wynT5+GSqVCt27dkJmZafEYmPwRERGRuAgW2kz08OFDDB06FGvWrIGzs/O/4QgCFi9ejIiICLzyyito3Lgx1q9fj+zsbGzatKn877MUTP6IiIhIVCQay2wAoFardba8vLxSzzthwgS8/PLL6Nq1q055YmIiUlJS0L17d22ZXC5Hhw4dcOLECYu/fyZ/REREROXk6ekJpVKp3WJiYgzW27JlC86cOWNwf0pKCgDAzc1Np9zNzU27z5K42peIiIjExYI3eb558yacnJy0xXK5XK/qzZs38e6772Lfvn1QKBSlNimRSHRPIQh6ZZbA5I+IiIhExZLP9nVyctJJ/gw5c+YMUlNT0bJlS21ZUVERjh49is8++wwJCQkAinsA3d3dtXVSU1P1egMtgcO+REREJC4l9/kzdzNSly5d8Pvvv+P8+fParVWrVhg6dCjOnz8PHx8fqFQq7N+/X3tMfn4+jhw5gqCgIIu/ffb8EREREVUgR0dHNG7cWKfM3t4erq6u2vLQ0FBER0fD19cXvr6+iI6Ohp2dHYYMGWLxeJj8ERERkahYctjXUsLDw5GTk4OQkBCkpaWhdevW2LdvHxwdHS17IjD5IyIiIrGx4IKP8jp8+LDOa4lEgsjISERGRprXsBE454+IiIhIRNjzR0RERKLyLA77Pk1M/oiIiEhcTFytW2obVRSHfYmIiIhEhD1/REREJCoc9iUiIiISk2dgtW9l4rAvERERkYiw54+IiIhEhcO+RERERGKiEYo3c9uoopj8ERERkbhwzh8RERERiQV7/oiIiEhUJLDAnD+LRFI5mPwRERGRuPAJH0REREQkFuz5IyIiIlHhrV6IiIiIxISrfYmIiIhILNjzR0RERKIiEQRIzFywYe7xlYnJHxEREYmL5n+buW1UURz2JSIiIhIR9vwRERGRqHDYl4iIiEhMRL7al8kfERERiQuf8EFEREREYsGeP6IqIKD5fQwY+jfqN8iAa408fDS1FU4eVWn3v/fBeXR9+ZbOMX9crIawse2edqhkYcPeu4Nh7yXrlD1ItcaQVs0qKSKqSL2H38PLw+/DzTMfAHA9QYGvFrkh/pBTJUf2fOETPojomadQFCHxTycc2O2JiI/PGKwT/0sNLJ7zb0JQUMiO/edFUoIC04f4aV9riioxGKpQd5NtsDbaHXeS5ACAbgMfIHJdEiZ098P1q4pKju45wmFfepxEIilzGzlypLbu7t270bFjRzg6OsLOzg6BgYGIi4vT7r9w4QLkcjl27typc45t27ZBoVDg4sWLAIDIyEg0b95cp45arUZERAT8/f2hUCigUqnQtWtXbN++HUIpP3RFRUWIiYmBv78/bG1t4eLigjZt2mDdunXaOiNHjtS+FxsbG/j4+GDy5MnIysoCACQlJZX63k+ePAkAiIuLM7hfodD95ZSSkoKJEyfCx8cHcrkcnp6e6NOnD3766SdtHW9vbyxevFjvvRi6JmJ15mRNbPzcHyeOuJdapyBfirQHCu32UC17ihFSRSoqlCDtro12y3hgU9khUQU5tV+J0wedcPuaHLevyRE3zx25WVL4t8yq7NDoOcKePwOSk/8dYtm6dStmzpyJhIQEbZmtrS0AYNmyZQgNDcXUqVOxYsUKyGQy7NixA+PHj8fFixexYMECNGvWDB9++CHeeustBAcHw9XVFampqRg/fjxmz56Nxo0bG4whPT0d7dq1Q0ZGBubMmYPAwEBYW1vjyJEjCA8PR+fOnVGtWjW94yIjI/H555/js88+Q6tWraBWqxEfH4+0tDSdej179sS6detQUFCAY8eOYcyYMcjKysLKlSu1dQ4cOICAgACd41xdXbX/7+TkpHNdgOLEuURSUhKCg4NRrVo1zJ8/H02bNkVBQQH27t2LCRMm4I8//ijtI6ByaPLCfXz1/T5kPbTB7+dcsGG1PzLS5JUdFllArbp5+Or0byjIk+CP8/aIm18LKTf42T7vpFIB/+mTDrmdBlfi7Ss7nOeKRFO8mdtGVcXkzwCV6t+5VEqlEhKJRKcMAG7evImwsDCEhoYiOjpaWx4WFgaZTIZJkyZh4MCBaN26NaZPn46dO3diwoQJ2LJlC8aNGwdfX19Mnjy51BhmzJiBpKQkXL16FR4eHtpyPz8/DB48WK+HrcSuXbsQEhKCgQMHasuaNdOfGySXy7XvaciQITh06BC+++47neTP1dVV730/ytB1eVRISAgkEgl+/fVX2Nv/+4srICAAb775ZqnHkenif6mJ4wfdkZpiCzePHLwxNgHRy07i3VHtUFhgVdnhkRn+OGePT97zxu1rCjjXKMDgiclYuP0PjOsagMx0/gp/Hnn752Dxrr8gk2uQkyVF1Ghv3PiTQ74WxWFfKo9vvvkGBQUFBhO4cePGwcHBAZs3bwYAWFlZYf369dixYweGDBmCvXv3Ii4uDlZWhv8oazQabNmyBUOHDtVJ/Eo4ODjA2trwL32VSoWDBw/i7t27Jr0fW1tbFBQUmHRMWR48eIA9e/ZgwoQJOolfCUO9lqbKy8uDWq3W2cTq2E8eOH3CDdevOeHX426Y+f6LqFXnIV4MSq3s0MhM8YeV+PlHZyQl2OLccSd8OLI+AKDbq/crOTKqKLf+liOkmx/e7e2L3RuqY/KSG6jjm1vZYdFzhMlfOV29ehVKpRLu7vpzsGQyGXx8fHD16lVtWcOGDREaGorNmzcjMjISfn5+eseVuHfvHtLS0uDv729yXAsXLsTdu3ehUqnQtGlTjB8/Hj/++GOZx/z666/YtGkTunTpolMeFBQEBwcHna2o6N+Z5hkZGXr7u3fvDgD466+/IAiC0e9h6tSpem092qNqSExMDJRKpXbz9PQ06lxikHZfgdQUW3h4cp7Q8yYvxwpJCbbwqMtk4HlVWCDFnSQ5/vzNDuti3JF42Rb9x5j2D3p6AsFCWxXFMYMKIgiCzvy3hw8fYuvWrbCzs8OxY8cQHh5e5rGA7vw5YzVq1AgXL17EmTNncPz4cRw9ehR9+vTByJEj8cUXX2jr7d69Gw4ODigsLERBQQH69euHZcuW6bS1detWNGzYUKfs0d5KR0dHnD17Vmd/yXxIU9/DlClTdBbSAMDSpUtx9OjRUo+ZPn063n//fe1rtVrNBPB/HJ3yUaNmLh7c51DR88ZGpoFn/Vxc/NWhskOhp8hGVoUzjWcQH+9G5eLn54eMjAzcuXNHb2g2Pz8f165dQ+fOnbVlU6ZMgUwmw4kTJ9C2bVts2LABw4cPN9h2jRo14OzsjCtXrpQrNqlUisDAQAQGBuK9997Dl19+iTfeeAMRERGoW7cuAKBTp05YuXIlbGxs4OHhARsb/dWDnp6eqF+/fpnnKW2/r68vJBIJrly5gv79+z8x5urVq+u15eLiUuYxcrkccrk4Jr0rbAvhUfvfXjyVRzZ8fDOQqZYhU22DoWOu4udD7nhwTw4392yMeDsB6gwZfjlS+pxMqhrGRNzCqQNKpN6RoZprIQZPSoadQxEOfOP65IOpyhk1LRmnDzri7h0ZbB2K0LFfOpoGPcQHQ30qOzR6jjD5K6cBAwYgPDwcn376KT799FOdfatWrUJWVhYGDx4MANi/fz+++OILHDt2DM2aNUN0dDRCQ0PRrVs3g8PGUqkUr7/+OjZu3IhZs2bpJZdZWVmQy+Wlzvt7XKNGjbTHlbC3ty8zsTOXi4sLevTogeXLl2PSpEl68/7S09MtMu9PLHz90/HxipPa12PfvQwAOPB9bSz/pAm8fDLRuect2DsWIO2eAr+ddcXHH7yAnGx+xau66u75mPZZIpycC5HxwBp/nLXHe/39kXpbHP/wEZtqNQoxZdkNuNQsRHamFRKvKPDBUB+cPepY2aE9X0S+4IN/GcqpTp06mD9/PiZPngyFQoE33ngDNjY22LFjB2bMmIGwsDC0bt0aarUao0ePxuTJk9GmTRsAwKRJk7Bt2za89dZb2LVrl8H2o6OjcfjwYbRu3Rpz585Fq1atYGNjg2PHjiEmJganT582mDy9+uqrCA4ORlBQEFQqFRITEzF9+nT4+fmZPIfw/v37SElJ0SmrVq2adqWxIAh6+wGgZs2akEqlWLFiBYKCgvDiiy8iKioKTZs2RWFhIfbv34+VK1eWu2dTjH4/Vx0vt+1d6v6Z77V+itHQ0/TxO+zxEZNFYZy68lQIAMy9VUvVzf2Y/JnjvffeQ7169bBgwQIsWbIERUVFCAgIwMqVKzFq1CgAQGhoKJRKJWbPnq09TiqVYt26dWjWrFmpw7/Ozs44efIkPv74Y8yZMwfXr1+Hs7MzmjRpgk8++QRKpdJgTD169MDmzZsRExODjIwMqFQqdO7cGZGRkUb3FJbo2rWrXtnmzZsxaNAgAMVz7Az1XCYnJ0OlUqFu3bo4e/Ys5s6di7CwMCQnJ6NGjRpo2bKlzi1liIiIniaxz/mTCKU9KoKoilGr1VAqlehaJwTWUg6JPe+Kbic/uRI9N4TCwsoOgZ6CQqEAh7EDGRkZcHKy/POMS/5OdG4xDdZW5i2IKyzKxcFzH1dYrBWJPX9EREQkLgIsMOfPIpFUCiZ/REREJC4iX/DBmzwTERERiQh7/oiIiEhcNABMf46CfhtVFJM/IiIiEhWxr/blsC8RERFRBYuJiUFgYCAcHR1Rs2ZN9O/fHwkJCTp1BEFAZGQkPDw8YGtri44dO+LSpUsWj4XJHxEREYlLyYIPczcTHDlyBBMmTMDJkyexf/9+FBYWonv37jpP35o/fz4WLlyIzz77DKdPn4ZKpUK3bt2QmZlp0bfPYV8iIiISl0pY7btnzx6d1+vWrUPNmjVx5swZtG/fHoIgYPHixYiIiMArr7wCAFi/fj3c3NywadMmjBs3zrx4H8GePyIiIqKnLCMjAwDg4uICAEhMTERKSgq6d++urSOXy9GhQwecOHHCoudmzx8RERGJiwV7/tRqtU6xXC6HXF72U6YEQcD777+Pdu3aoXHjxgCAlJQUAICbm5tOXTc3N1y/ft28WB/Dnj8iIiISF42FNgCenp5QKpXaLSYm5omnf+edd/Dbb79h8+bNevskEt170AiCoFdmLvb8ERERkahY8lYvN2/e1Hm275N6/SZOnIidO3fi6NGjqF27trZcpVIBKO4BdHd315anpqbq9Qaaiz1/REREROXk5OSks5WW/AmCgHfeeQfbt2/HwYMHUbduXZ39devWhUqlwv79+7Vl+fn5OHLkCIKCgiwaM3v+iIiISFwqYbXvhAkTsGnTJuzYsQOOjo7aOX5KpRK2traQSCQIDQ1FdHQ0fH194evri+joaNjZ2WHIkCHmxfoYJn9EREQkLhoBkJiZ/GlMO37lypUAgI4dO+qUr1u3DiNHjgQAhIeHIycnByEhIUhLS0Pr1q2xb98+ODo6mhfrY5j8EREREVUwwYieQolEgsjISERGRlZoLEz+iIiISFwqYdj3WcLkj4iIiETGAskfqm7yx9W+RERERCLCnj8iIiISFw77EhEREYmIRoDZw7YmrvZ9lnDYl4iIiEhE2PNHRERE4iJoijdz26iimPwRERGRuHDOHxEREZGIcM4fEREREYkFe/6IiIhIXDjsS0RERCQiAiyQ/FkkkkrBYV8iIiIiEWHPHxEREYkLh32JiIiIRESjAWDmffo0Vfc+fxz2JSIiIhIR9vwRERGRuHDYl4iIiEhERJ78cdiXiIiISETY80dERETiIvLHuzH5IyIiIlERBA0EwbzVuuYeX5mY/BEREZG4CIL5PXec80dEREREVQF7/oiIiEhcBAvM+avCPX9M/oiIiEhcNBpAYuacvSo854/DvkREREQiwp4/IiIiEhcO+xIRERGJh6DRQDBz2Lcq3+qFw75EREREIsKePyIiIhIXDvsSERERiYhGACTiTf447EtEREQkIuz5IyIiInERBADm3uev6vb8MfkjIiIiURE0AgQzh30FJn9EREREVYSggfk9f7zVCxERERFVAez5IyIiIlHhsC8RERGRmIh82JfJHz03Sv4VVqjJr+RI6GkoEgoqOwR6igShsLJDoKegEMXf64ruVStEgdn3eC6JtSpi8kfPjczMTADA4VtfVHIkRERkjszMTCiVSou3K5PJoFKpcDzlB4u0p1KpIJPJLNLW0yQRqvKgNdEjNBoN7ty5A0dHR0gkksoO56lRq9Xw9PTEzZs34eTkVNnhUAXiZy0eYv2sBUFAZmYmPDw8IJVWzJrU3Nxc5OdbZoRIJpNBoVBYpK2niT1/9NyQSqWoXbt2ZYdRaZycnET1R0LM+FmLhxg/64ro8XuUQqGokgmbJfFWL0REREQiwuSPiIiISESY/BFVcXK5HLNmzYJcLq/sUKiC8bMWD37WVJG44IOIiIhIRNjzR0RERCQiTP6IiIiIRITJHxEREZGIMPkjIiIiEhEmf0T/M3LkSPTv31+v/PDhw5BIJEhPT9fb16BBA8hkMty+fVunbllbXFxcmfVSUlJKjXHbtm1o3bo1lEolHB0dERAQgLCwMO3+uLg4nbbc3d3x2muvITExUVvH29vb4Hk//vhjAEBSUlKpsZ08eVLbTn5+PubPn49mzZrBzs4O1atXR3BwMNatW4eCgoJyX9OyPo9vvvkGCoUC8+fPBwBERkYajNPf3197TMeOHREaGqrzWiKRYMuWLTptL168GN7e3qVey5KtrJvDlvW+vL29sXjxYr3y6OhoWFlZaa9/Sd2yfoY6duxYZr1H23rctWvXMHjwYHh4eEChUKB27dro168frl69qq3zaFuOjo5o1aoVtm/frt1v7HU3VGf8+PE68Rw6dAgvvfQSXF1dYWdnh0aNGiEsLEzvO2XsNX3S92/kyJHaurt370bHjh3h6OgIOzs7BAYGIi4uTrv/woULkMvl2Llzp845tm3bBoVCgYsXL2qvR/PmzXXqqNVqREREwN/fHwqFAiqVCl27dsX27dtLfW5tUVERYmJi4O/vD1tbW7i4uKBNmzZYt26dts7IkSO178XGxgY+Pj6YPHkysrKyABj3/TX2ZzslJQUTJ06Ej48P5HI5PD090adPH/z0009lfgalXRN6dvAJH0TldPz4ceTm5mLgwIGIi4tDREQEgoKCkJycrK3z7rvvQq1W6/zyViqVOHXqFAAgISFB7+79NWvWNHi+AwcOYNCgQYiOjkbfvn0hkUhw+fJlnV/EQPETARISEiAIAv744w+MGzcOffv2xfnz52FlZQUAiIqKwtixY3WOc3R01DtfQECATpmrqyuA4sSvR48euHDhAj766CMEBwfDyckJJ0+exIIFC9CiRQuL/+L/4osvMGHCBCxfvhxjxozRlgcEBODAgQM6da2ty/7VplAo8MEHH2DAgAGwsbEptV7JtXyUpR8duG7dOoSHh2Pt2rWYNm0aAOD06dMoKioCAJw4cQIDBgzQ+Vl59FmixnyWJfLz89GtWzf4+/tj+/btcHd3x61bt/DDDz8gIyNDL66ePXsiPT0dn3zyCQYOHIjjx4+jbdu2AIy77mPHjkVUVJROmZ2dnfb/V69ejZCQEIwYMQLbtm2Dt7c3bty4gQ0bNuDTTz/FwoULy754Bjz6/du6dStmzpyp8xna2toCAJYtW4bQ0FBMnToVK1asgEwmw44dOzB+/HhcvHgRCxYsQLNmzfDhhx/irbfeQnBwMFxdXZGamorx48dj9uzZaNy4scEY0tPT0a5dO2RkZGDOnDkIDAyEtbU1jhw5gvDwcHTu3BnVqlXTOy4yMhKff/45PvvsM7Rq1QpqtRrx8fFIS0vTqdezZ0/tP7KOHTuGMWPGICsrCytXrtTWKev7Czz5ZzspKQnBwcGoVq0a5s+fj6ZNm6KgoAB79+7FhAkT8Mcff5T2EVAVwOSPqJxiY2MxZMgQdOjQARMmTMCMGTO0Dw0vYWtri7y8PJ2yR9WsWdPgHwFDdu/ejXbt2mHKlCnaMj8/P73eMYlEoj2fu7s7Zs2ahWHDhuGvv/5CgwYNABQnB6XFVMLV1bXUOosXL8bRo0cRHx+PFi1aaMt9fHwwcOBAiz03s8T8+fMxc+ZMbNq0CQMGDNDZZ21t/cT38rjBgwdj165dWLNmDUJCQkqt9+i1rAhHjhxBTk4OoqKisGHDBhw9ehTt27dHjRo1tHVcXFwAlP6zYsxnWeLy5cu4du0aDh48CC8vLwCAl5cXgoOD9epWq1YNKpUKKpUKq1atwpYtW7Bz505t8mfMdbezsyu1zq1btzBp0iRMmjQJixYt0pZ7e3ujffv2ZfYKl+XR8ymVSoOf4c2bNxEWFobQ0FBER0dry8PCwiCTyTBp0iQMHDgQrVu3xvTp07Fz505MmDABW7Zswbhx4+Dr64vJkyeXGsOMGTOQlJSEq1evwsPDQ1vu5+eHwYMHl9p7vGvXLoSEhGDgwIHasmbNmunVk8vl2vc0ZMgQHDp0CN99951O8lfW9xd48s92SEgIJBIJfv31V9jb22vLAwIC8Oabb5Z6HFUNHPYlKofMzEx8/fXXGDZsGLp164asrCwcPny4Qs+pUqlw6dIl7VCTsUp6OkqGYi3hq6++QteuXXUSvxI2NjY6fyzMNW3aNHz00UfYvXu3XuJXXk5OTpgxYwaioqK0w2WVITY2FoMHD4aNjQ0GDx6M2NjYCj1fjRo1IJVK8c0332h7Fo1hY2MDa2tri/4Mff3118jPz0d4eLjB/cb+o6g8vvnmGxQUFBhM4MaNGwcHBwds3rwZAGBlZYX169djx44dGDJkCPbu3Yu4uDhtL/rjNBoNtmzZgqFDh+okfiUcHBxK7ZlWqVQ4ePAg7t69a9L7sbW1tehn8+DBA+zZswcTJkww+F2uyM+Gng4mf0SP2L17NxwcHHS2Xr166dXbsmULfH19ERAQACsrKwwaNKhcf7hr166tc66SnjlDJk6ciMDAQDRp0gTe3t4YNGgQ1q5di7y8vFKPuXXrFj755BPUrl0bfn5+2vKpU6fqvc/Hk9egoCC9OiUJw59//qkzv6ssxl5TQ3788UfMmzcPO3bsQNeuXQ3W+f333/Xaf3RYuDQhISFQKBRlDi1mZGTotd29e/cntv345+rg4IAbN27o1FGr1di2bRuGDRsGABg2bBi++eYbqNXqJ7b/KGM+yxK1atXC0qVLMXPmTDg7O6Nz58746KOPcO3atVLbz8vLw5w5c6BWq9GlSxdtuTHXfcWKFXp11q9fD6D4Z8jJyQnu7u5GvU9jrqmxrl69CqVSafDcMpkMPj4+OnMgGzZsiNDQUGzevBmRkZE636XH3bt3D2lpaUZ/Px61cOFC3L17FyqVCk2bNsX48ePx448/lnnMr7/+ik2bNul8NkDZ31+g7J/tv/76C4IgGP0eDP0MPtqjSs8eDvsSPaJTp046QycAcOrUKe0f6BKxsbE6ZcOGDdMOVZnyr+Jjx47pzM8qa66avb09vv/+e/z99984dOgQTp48ibCwMCxZsgS//PKLdi5VyS91QRCQnZ2NF154Adu3b9eZJzZlyhSdie9AcWLwqK1bt6Jhw4Y6ZSW9HYIgGD33zdhrakjTpk1x7949zJw5E4GBgQbnsjVo0EBvQn5pc94eJZfLERUVhXfeeQdvv/22wTqOjo44e/asTllJT2pZHv9cAWgXaZTYtGkTfHx8tMN6zZs3h4+PD7Zs2YK33nrriecoYcxn+agJEyZg+PDhOHToEE6dOoWvv/4a0dHR2LlzJ7p166atN3jwYFhZWSEnJwdKpRILFizQSdqNue5Dhw5FRESETlnJnFZTfoYA466ppTwe28OHD7F161bY2dnh2LFjpfZWlhwLlG9uaKNGjXDx4kWcOXMGx48fx9GjR9GnTx+MHDkSX3zxhbZeyT+oCgsLUVBQgH79+mHZsmU6bZX1/QXK/tk29T0Y+hlcunQpjh49atTx9PQx+SN6hL29PerXr69TduvWLZ3Xly9fxqlTp3D69GlMnTpVW15UVITNmzeXmkgYUrduXZOHUOrVq4d69ephzJgxiIiIgJ+fH7Zu3YpRo0YB+PeXulQqhZubm8Fhm+rVq+u9z8d5enqWWsfPzw9XrlwxKl5jrmlpatWqhW3btqFTp07o2bMn9uzZo5cAyGSyJ76X0gwbNgwLFizAnDlzdFb6lpBKpeVq29Dn+nhiv3btWly6dEmnXKPRIDY21qTkz5jP8nGOjo7o27cv+vbtizlz5qBHjx6YM2eOTvK3aNEidO3aFU5OTgYXIRlz3ZVKZZk/QxkZGUhOTjaq98+Ya2qsknPfuXNHb2g2Pz8f165dQ+fOnbVlU6ZMgUwmw4kTJ9C2bVts2LABw4cPN9h2jRo14OzsbPT343FSqRSBgYEIDAzEe++9hy+//BJvvPEGIiIiULduXQD//oPKxsYGHh4eBhctlfX9LTlPaft9fX0hkUhw5coVg6v1H2foZ7Bkrio9mzjsS2Si2NhYtG/fHhcuXMD58+e1W3h4eIXP2Xqct7c37OzsdOatlfxS9/Hxsejcu0cNGTIEBw4cwLlz5/T2FRYWWnQeXZ06dXDkyBGkpqaie/fuJg+LlkUqlSI6OhorV65EUlKSxdp9kt9//x3x8fE4fPiwzs/Q0aNHcfr0aZPndZqj5BYtj39mKpUK9evXL3X1ubleffVVyGQy7W17HlfeBR/GGDBgAKytrfHpp5/q7Vu1ahWysrIwePBgAMD+/fvxxRdfIC4uDs2aNUN0dDRCQ0N1VhU/SiqV4vXXX8dXX32FO3fu6O3PyspCYWGh0bE2atRIe1yJkn9QeXl5lblavbxcXFzQo0cPLF++3OB3uSI/G3o62PNHZIKCggJs3LgRUVFRerd5GDNmDObPn48LFy4YXKFnSGpqKnJzc3XKXF1dDf5Cj4yMRHZ2Nl566SV4eXkhPT0dS5cuRUFBgU6PjTEyMzP17idoZ2enc9uZ+/fv69WpVq0aFAoFQkND8f3336NLly746KOP0K5dOzg6OiI+Ph7z5s1DbGysRW/1Urt2bRw+fBidOnVC9+7dsXfvXiiVSgDFyebjcUokEri5uRnVdu/evdG6dWusXr1a7xhBEAzed7FmzZqQSsv/b+fY2Fi8+OKLaN++vd6+tm3bIjY2VmcFbFmM+SxLnD9/HrNmzcIbb7yBRo0aQSaT4ciRI1i7dq1OL7YxjLnu2dnZenXkcjmcnZ3h6emJRYsW4Z133oFarcbw4cPh7e2NW7duYcOGDXBwcDCYnFlCnTp1MH/+fEyePBkKhQJvvPEGbGxssGPHDsyYMQNhYWFo3bo11Go1Ro8ejcmTJ6NNmzYAgEmTJmHbtm146623sGvXLoPtR0dH4/Dhw2jdujXmzp2LVq1awcbGBseOHUNMTAxOnz5tsMf/1VdfRXBwMIKCgqBSqZCYmIjp06fDz8/P5DmEZX1/gSf/bK9YsQJBQUF48cUXERUVhaZNm6KwsBD79+/HypUry92zSc8IgYgEQRCEESNGCP369dMrP3TokABASEtLE7755htBKpUKKSkpBtto0qSJMHHiRKPbNLT98ssvBts+ePCgMGDAAMHT01OQyWSCm5ub0LNnT+HYsWPaOuvWrROUSmWZ79PLy8vgeceNGycIgiAkJiaWGtvmzZu17eTm5goxMTFCkyZNBIVCIbi4uAjBwcFCXFycUFBQYPQ1LY2hY+/cuSM0aNBACAwMFNLS0oRZs2YZjFMul2uP6dChg/Duu++W+loQBOHEiRMCAMHLy0vnWpZ2HZKTkw3GXNb78vLyEhYtWiTk5eUJrq6uwvz58w228emnnwrVq1cX8vLyjGqzrM/ycXfv3hUmTZokNG7cWHBwcBAcHR2FJk2aCAsWLBCKioq09QAI3377rcE2BEEw+robqtOjRw+dtvbv3y/06NFDcHZ2FhQKheDv7y9MnjxZuHPnjtHXtDRP+j7s2LFD+M9//iPY29sLCoVCaNmypbB27Vrt/lGjRgmNGzfWfhYl/vzzT8HOzk5Yv3699no0a9ZMp056erowbdo0wdfXV/t97dq1q/Dtt98KGo3GYDyff/650KlTJ6FGjRqCTCYT6tSpI4wcOVJISkrS1intO1XCmO+vsT/bd+7cESZMmCB4eXkJMplMqFWrltC3b1/h0KFD2jqlfQaGrgk9OySCUMqtxomIiIjoucM5f0REREQiwuSPiIiISESY/BERERGJCJM/IiIiIhFh8kdEREQkIkz+iIiIiESEyR8RERGRiDD5IyKyoMjISJ2nm4wcOdKo56NaWlJSEiQSCc6fP19qHW9vbyxevNjoNuPi4kx+FrUhEokE3333ndntEFH5MPkjoufeyJEjIZFIIJFIYGNjAx8fH0yePNmizyAuzZIlSxAXF2dUXWMSNiIic/HZvkQkCj179sS6detQUFCAY8eOYcyYMcjKysLKlSv16hYUFBh8vnJ5lDyDmIjoWcGePyISBblcDpVKBU9PTwwZMgRDhw7VDj2WDNWuXbsWPj4+kMvlEAQBGRkZeOutt1CzZk04OTmhc+fOuHDhgk67H3/8Mdzc3ODo6IjRo0cjNzdXZ//jw74ajQbz5s1D/fr1IZfLUadOHcydOxcAULduXQBAixYtIJFI0LFjR+1x69atQ8OGDaFQKODv748VK1bonOfXX39FixYtoFAo0KpVK5w7d87ka7Rw4UI0adIE9vb28PT0REhICB4+fKhX77vvvoOfnx8UCgW6deuGmzdv6uzftWsXWrZsCYVCAR8fH8yePRuFhYUmx0NEFYPJHxGJkq2tLQoKCrSv//rrL/z3v//Ftm3btMOuL7/8MlJSUvDDDz/gzJkzeOGFF9ClSxc8ePAAAPDf//4Xs2bNwty5cxEfHw93d3e9pOxx06dPx7x58/Dhhx/i8uXL2LRpE9zc3AAUJ3AAcODAASQnJ2P79u0AgDVr1iAiIgJz587FlStXEB0djQ8//BDr168HAGRlZaF3795o0KABzpw5g8jISEyePNnkayKVSrF06VJcvHgR69evx8GDBxEeHq5TJzs7G3PnzsX69evx888/Q61WY9CgQdr9e/fuxbBhwzBp0iRcvnwZq1evRlxcnDbBJaJngEBE9JwbMWKE0K9fP+3rU6dOCa6ursJrr70mCIIgzJo1S7CxsRFSU1O1dX766SfByclJyM3N1WmrXr16wurVqwVBEIS2bdsK48eP19nfunVroVmzZgbPrVarBblcLqxZs8ZgnImJiQIA4dy5czrlnp6ewqZNm3TKPvroI6Ft27aCIAjC6tWrBRcXFyErK0u7f+XKlQbbepSXl5ewaNGiUvf/97//FVxdXbWv161bJwAQTp48qS27cuWKAEA4deqUIAiC8J///EeIjo7WaWfjxo2Cu7u79jUA4dtvvy31vERUsTjnj4hEYffu3XBwcEBhYSEKCgrQr18/LFu2TLvfy8sLNWrU0L4+c+YMHj58CFdXV512cnJy8PfffwMArly5gvHjx+vsb9u2LQ4dOmQwhitXriAvLw9dunQxOu67d+/i5s2bGD16NMaOHastLyws1M4nvHLlCpo1awY7OzudOEx16NAhREdH4/Lly1Cr1SgsLERubi6ysrJgb28PALC2tkarVq20x/j7+6NatWq4cuUKXnzxRZw5cwanT5/W6ekrKipCbm4usrOzdWIkosrB5I+IRKFTp05YuXIlbGxs4OHhobegoyS5KaHRaODu7o7Dhw/rtVXe253Y2tqafIxGowFQPPTbunVrnX1WVlYAAEEQyhXPo65fv46XXnoJ48ePx0cffQQXFxccP34co0eP1hkeB4pv1fK4kjKNRoPZs2fjlVde0aujUCjMjpOIzMfkj4hEwd7eHvXr1ze6/gsvvICUlBRYW1vD29vbYJ2GDRvi5MmTGD58uLbs5MmTpbbp6+sLW1tb/PTTTxgzZozefplMBqC4p6yEm5sbatWqhWvXrmHo0KEG223UqBE2btyInJwcbYJZVhyGxMfHo7CwEJ9++imk0uLp4P/973/16hUWFiI+Ph4vvvgiACAhIQHp6enw9/cHUHzdEhISTLrWRPR0MfkjIjKga9euaNu2Lfr374958+ahQYMGuHPnDn744Qf0798frVq1wrvvvosRI0agVatWaNeuHb766itcunQJPj4+BttUKBSYOnUqwsPDIZPJEBwcjLt37+LSpUsYPXo0atasCVtbW+zZswe1a9eGQqGAUqlEZGQkJk2aBCcnJ/Tq1Qt5eXmIj49HWloa3n//fQwZMgQREREYPXo0PvjgAyQlJWHBggUmvd969eqhsLAQy5YtQ58+ffDzzz9j1apVevVsbGwwceJELF26FDY2NnjnnXfQpk0bbTI4c+ZM9O7dG56enhg4cCCkUil+++03/P7775gzZ47pHwQRWRxX+xIRGSCRSPDDDz+gffv2ePPNN+Hn54dBgwYhKSlJuzr39ddfx8yZMzF16lS0bNkS169fx9tvv11mux9++CHCwsIwc+ZMNGzYEK+//jpSU1MBFM+nW7p0KVavXg0PDw/069cPADBmzBh88cUXiIuLQ5MmTdChQwfExcVpbw3j4OCAXbt24fLly2jRogUiIiIwb948k95v8+bNsXDhQsybNw+NGzfGV199hZiYGL16dnZ2mDp1KoYMGYK2bdvC1tYWW7Zs0e7v0aMHdu/ejf379yMwMBBt2rTBwoUL4eXlZVI8RFRxJIIlJosQERERUZXAnj8iIiIiEWHyR0RERCQiTP6IiIiIRITJHxEREZGIMPkjIiIiEhEmf0REREQiwuSPiIiISESY/BERERGJCJM/IiIiIhFh8kdEREQkIkz+iIiIiESEyR8RERGRiPw/08oyrwmbmKsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dh0VFRSgqKtI8fnyKe32juGsGtUoEe2ft18DepQT3MvX/Gg/smI/pX9xATIQ3iovEUClF6Byeiwlz08o9z6qPPeDkXoKnutw3av3JcHJZEczMBNzLtdJKv5crhYN9QZXLdXe7j/Yu6fj1oC9mxvZEYw8F3h5zBGZiAd9sblfdapMBZI5KmJkDOXe0v15z7ljAwUX/Z5SDawly9soey28OcwtA7qjE3Uzdz8rXZ9xEdoYEJw6W11InYNysmzhzxAY3LlqVk8d0qY1wr9uqLJgMAJGRkYiMjNS7LzExUSdNLpcjP7/qY6r5E68KduzYAVtbW62tb9++Ovk2bNiAFi1aIDAwEGZmZhgyZAgSEhIqfb4mTZponausxU2ft99+Gx07dkSbNm3g7e2NIUOGYNWqVVoB0ePS0tLw6aefokmTJvDz89OkT58+Xec69+7dq3VsaGioTh6VqvRX5OXLl+Hv72/QNRr6nD4qNjZWa0q7p6dnhfnri8cnXwmCCCinh+DGJUvEfdAEr76TgS92XcTH6/7B7VQJlk7X/1x8t8wVe7Y6YNZX1yCR1o8bcjdkwmMvgUhPWmWIRUCOwgqLV4Tg8jUn7D3kg3Vb2uDF8IvVqidVns5rKxKACl5bndddVE46gEFvZqDbgHuIfsMHJUX6v8YnzE2FT0ABYt/y0bvf1KkFsVG2+oAtelXQrVs3nVWsjxw5guHDh2ulJSQkaKUNHz4cXbt2RU5OjsFdsQBw4MABrbFx5uYVNNXb2ODHH3/EP//8gz179uDw4cOYPHkylixZgj/++APW1qWDsnNzc2FrawtBEJCfn4+nnnoKW7Zs0ZrNM3XqVIwePVqr/MaNtceXJCUlISAgQCutbIyfoVPGAcOf00fNmDEDkyZN0jxWKBT1OtiTOaogNhNw7472r/fcLHM4uCj1HpP0uRsCO+ZhUGRpd59vq0JIrdIw+T8tMGp6OpzcHh63Md4FGz53wydJV+DbqrDmLoSeKFdhCZVKBMfHWu/s5YXIya1668vdHCsoldpfQCk35XByKIC5mQpKlVmVyybDKO6aQ6UEHFy137NyZyXuZenvxbiXaQEH18da8p2VUJYAinvan/cvj7+NIW/dxrtDm+Paef2TbCI/SkVIeC4mD/RDVnrlZmhSw8NArwpsbGzQvLn2oNe0NO2usnPnzuHIkSM4evQopk+frklXqVRYv3493nzzTYPP5+PjU6nAEACaNWuGZs2aYezYsZg5cyb8/PyQlJSkuW2KnZ0dTpw4AbFYDDc3N9jY6I4Bc3Z21rnOx3l6epabx8/PD+fPnzeovoY8p48rb/Xx+spCIqBF23yc2G+HsL65mvQT++0Q0jtX7zGFBWKYmWn/5BeXPX4keWOcC9YtcUfMun/g167qXYNkHEqVGS5ddcJTbdPx+9GHK9w/1fYWDh2t+o+Vsxdd0S3sKkQiobQlGEATDwWy71oxyKslyhIxLv9tjae6KHBol70m/aku9/HHz3K9x5w/YYNOPbXf40FdFbj0l43W+LyXI25j2MR0vDe8BS7/pW/croAJc9MQ2icHUwe1wO3UhvP5aGwqiKAqr6ukEmXUB/Wj3bEeSkhIQNeuXXH69GmcOnVKs02bNq1K3bfV4e3tDWtra+TlPRwILBaL0bx5c/j6+uoN8oxh2LBh+OWXX3Dy5EmdfUqlUqs+VOr/xt3BrnWO2L3eESmXLbF8diNk3rTACyNLZ9WuivHA/IlNNfk791Lg95/ssX2NE9JvSHD2TxvEf9AELTvkwcm9tEXhu2WuWDPfA5MWpsDNsxh3M81xN9McBXl8+9elzTtaoW+Py+jd7TKaNs5BxKg/4eqchx3JpUMzXh96HNMmHNA6ppnXXTTzugsrqRJyWSGaed1F08Y5mv3bf24JmV0RIkf/icYeuXi6QxqG/udvbNtt2BAKMo4tK13RZ2g2wgdnwbN5AcbPToNr42L8+HXpLNzX3r2JqYuva/Lv+NoZbk2KMW5WGjybFyB8cBZ6D8nG5hUPZ88PejMDo6bewsIpXridKoGDSwkcXEogtX44qeqtj1PR/T938clb3ih4YKbJI5Gqa+3a6wt23VK1lJSU4Ouvv0Z0dLTOhIixY8di/vz5OH36NNq1M2xwdGZmJgoLtbvanJyc9E5mmDNnDvLz8/H888/Dy8sLOTk5WLp0KUpKStCrV69KXcf9+/d11uuztrbWWuolOztbJ4+9vT2kUimioqLw448/okePHvjoo4/wzDPPwM7ODseOHcO8efOQkJDA5VUe81z/HNy/Z4ZvF7njbqY5vFoWYu43V+HWpLRb526mBe7cfNgVEz74LgoeiLFttTO+/LAxbOQqtA+7jzEzHy7FsmONM0qKxZj7hvZYneGTMjBiypNvu0M1Y98fPpDZFWH4wNNwdCjA9VR7zIztoVkOxcmhAK7O2j+Gln+6XfN/v2bZ6NHlGjIybTDirZcBAHeybfDu3F54c9RRrPx0G7LuWuP7nwJ0ZvdSzdq33RF2Diq8GpVRumDyRSneH9kMmTdLW9gcXUvg0vjhqgO3Uy3x/shmGD87Df1G3cHd2xaIn9VEs7QKALw4MgsSSwEfrLymda6vF7rjm4WNAAD9RpX+IFywSXv5nQXveCF5o1ONXCv9+zHQqwHbtm1DdnY2/vOf/+jsa9GiBdq0aYOEhAQsXbrUoPL0Tb74448/0LlzZ530Z599FsuWLcPIkSNx+/ZtODg4oEOHDvj5558rnMShz6xZszBr1iyttPHjx2P58uWaxz179tQ5bv369RgyZAgsLS2RnJyMRYsWYcWKFZgyZQqsra0REBCAiRMnVmk9IFPQb3Q2+o3O1rtvyuIUnbT+Y7LQf0xWueWt/fOc0epGxrX9Z39s/1l/a9uncc/opPV6ZdQTyzx/2RUT33+h2nWj6tmx1gU71rro3ffZJG+dtL8P2+GtvgG6mf9nVMiTPy97N3nK4PqZOhWq3/VaXxaoEglCdeZ4Ef17KBQKyOVy3LvkC5ld/WhSp6rr9crouq4C1SLxob/rugpUC5RCCfaqtyA3N9eg24pVVtn3xPuHwyG1rd4SX4UPSjC38881VldjYYseERERmRSVIIaqmmPsqnt8bakftSQiIiKiSmOLHhEREZkUASKoqzlGT6gny6sw0CMiIiKTwq5bIiIiIqr32KJHREREJkUtiKAWqtf1Wt3jawsDPSIiIjIpKoihqmanZnWPry31o5ZEREREVGls0SMiIiKTwq5bIiIiogZKDTHU1ezUrO7xtaV+1JKIiIiIKo0tekRERGRSVIIIqmp2vVb3+NrCQI+IiIhMCsfoERERETVQgiCGupp3thB4ZwwiIiIiqkts0SMiIiKTooIIKlRzjF41j68tDPSIiIjIpKiF6o+xUwtGqkwNY9ctERERUQPFFj0iIiIyKWojTMao7vG1hYEeERERmRQ1RFBXc4xddY+vLfUjHCUiIiKiSmOLHhEREZkU3hmDiIiIqIEypTF69aOWRERERFRpbNEjIiIik6KGEe51W08mYzDQIyIiIpMiGGHWrcBAj4iIiOjfRy0YoUWvnkzG4Bg9IiIiogaKgR4RERGZlLJZt9XdqiIuLg4+Pj6QSqUICgrCgQMHKsxfVFSEmTNnwsvLC5aWlmjWrBlWrVpl8PnYdUtEREQmpa66bpOSkhAVFYW4uDiEhYVhxYoV6Nu3L86dO4emTZvqPeaVV17B7du3kZCQgObNmyMzMxNKpdLgczLQIyIiIqoihUKh9djS0hKWlpZ68y5cuBBjxozB2LFjAQCLFy/G7t27ER8fj9jYWJ38u3btwr59+3D16lU4OjoCALy9vStVP3bdEhERkUkpu9dtdTcA8PT0hFwu12z6AjYAKC4uxvHjxxEeHq6VHh4ejkOHDuk9Ztu2bQgODsb8+fPRuHFj+Pn5YcqUKSgoKDD4WtmiR0RERCbFmF23qampkMlkmvTyWvOysrKgUqng5uamle7m5oaMjAy9x1y9ehUHDx6EVCrF999/j6ysLERGRuLu3bsGj9NjoEdERERURTKZTCvQexKRSDvAFARBJ62MWq2GSCTCt99+C7lcDqC0+/fll1/GsmXLYGVl9cTzseuWiIiITEpZi151t8pwdnaGmZmZTutdZmamTitfGQ8PDzRu3FgT5AFAQEAABEFAWlqaQedloEdEREQmpS4CPYlEgqCgICQnJ2ulJycnIzQ0VO8xYWFhuHXrFh48eKBJu3TpEsRiMZo0aWLQeRnoEREREdWCSZMm4auvvsKqVatw/vx5vPPOO0hJSUFERAQAYMaMGRg5cqQm/7Bhw+Dk5ITXXnsN586dw/79+zF16lS8/vrrBnXbAhyjR0RERCamrtbRGzx4MLKzsxEdHY309HS0bt0aO3fuhJeXFwAgPT0dKSkpmvy2trZITk7G22+/jeDgYDg5OeGVV17B3LlzDT4nAz0iIiIyKQKgWR6lOmVURWRkJCIjI/XuS0xM1Enz9/fX6e6tDAZ6REREZFLqqkWvLnCMHhEREVEDxRY9IiIiMimm1KLHQI+IiIhMiikFeuy6JSIiImqg2KJHREREJsWUWvQY6BEREZFJEQQRhGoGatU9vraw65aIiIiogWKLHhEREZkUNUTVXjC5usfXFgZ6REREZFJMaYweu26JiIiIGii26BEREZFJMaXJGAz0iIiIyKSYUtctAz0iIiIyKabUoscxekREREQNFFv0qMEJWjsGYqm0rqtBNSxo3oW6rgLVouwwVV1XgWqDUDuvs2CErtv60qLHQI+IiIhMigBAEKpfRn3ArlsiIiKiBootekRERGRS1BBBxDtjEBERETU8nHVLRERERPUeW/SIiIjIpKgFEURcMJmIiIio4REEI8y6rSfTbtl1S0RERNRAsUWPiIiITIopTcZgoEdEREQmhYEeERERUQNlSpMxOEaPiIiIqIFiix4RERGZFFOadctAj4iIiExKaaBX3TF6RqpMDWPXLREREVEDxRY9IiIiMimcdUtERETUQAn/26pbRn3ArlsiIiKiBootekRERGRSTKnrli16REREZFoEI21VEBcXBx8fH0ilUgQFBeHAgQPl5t27dy9EIpHOduHCBYPPxxY9IiIiMi1GaNFDFY5PSkpCVFQU4uLiEBYWhhUrVqBv3744d+4cmjZtWu5xFy9ehEwm0zx2cXEx+Jxs0SMiIiKqIoVCobUVFRWVm3fhwoUYM2YMxo4di4CAACxevBienp6Ij4+v8Byurq5wd3fXbGZmZgbXj4EeERERmZSyO2NUdwMAT09PyOVyzRYbG6v3nMXFxTh+/DjCw8O10sPDw3Ho0KEK69uhQwd4eHigR48e2LNnT6WulV23REREZFKMORkjNTVVq1vV0tJSb/6srCyoVCq4ublppbu5uSEjI0PvMR4eHli5ciWCgoJQVFSEr7/+Gj169MDevXvRtWtXg+rJQI+IiIioimQymVag9yQikXaAKQiCTlqZli1bomXLlprHISEhSE1NxYIFCwwO9Nh1S0RERKZFEBlnqwRnZ2eYmZnptN5lZmbqtPJVpHPnzrh8+bLB+RnoERERkUkx5hg9Q0kkEgQFBSE5OVkrPTk5GaGhoQaXc/LkSXh4eBicn123RERERLVg0qRJGDFiBIKDgxESEoKVK1ciJSUFERERAIAZM2bg5s2bWLt2LQBg8eLF8Pb2RmBgIIqLi/HNN99g8+bN2Lx5s8HnZKBHREREpqWObnY7ePBgZGdnIzo6Gunp6WjdujV27twJLy8vAEB6ejpSUlI0+YuLizFlyhTcvHkTVlZWCAwMxI8//ojnn3/e4HOKBKGyjY9E/04KhQJyuRy+738MsVRa19WhGhbU3fCV4an+yw67V9dVoFqgFEqwF1uRm5tbqQkOhir7nmi6chbE1tX7nlDnFyJlXHSN1dVYDGrRW7p0qcEFTpw4scqVISIiIiLjMSjQW7RokUGFiUQiBnpERET072ci/ZkGBXrXrl2r6XoQERER1QpjLpj8b1fl5VWKi4tx8eJFKJVKY9aHiIiIqGYJRtrqgUoHevn5+RgzZgysra0RGBiomR0yceJEfPLJJ0avIBERERFVTaUDvRkzZuD06dPYu3cvpI/MbOzZsyeSkpKMWjkiIiIi4xMZafv3q/Q6ej/88AOSkpLQuXNnrXuztWrVCv/8849RK0dERERkdHW0jl5dqHSL3p07d+Dq6qqTnpeXV+5NeYmIiIio9lU60OvYsSN+/PFHzeOy4O7LL79ESEiI8WpGREREVBNMaDJGpbtuY2Nj0adPH5w7dw5KpRJLlizB2bNn8ccff2Dfvn01UUciIiIi4xFEpVt1y6gHKt2iFxoait9//x35+flo1qwZfv75Z7i5ueGPP/5AUFBQTdSRiIiIiKqg0i16ANCmTRusWbPG2HUhIiIiqnGCULpVt4z6oEqBnkqlwvfff4/z589DJBIhICAA/fv3h7l5lYojIiIiqj0mNOu20pHZmTNn0L9/f2RkZKBly5YAgEuXLsHFxQXbtm1DmzZtjF5JIiIiIqq8So/RGzt2LAIDA5GWloYTJ07gxIkTSE1NRdu2bTFu3LiaqCMRERGR8ZRNxqjuVg9UukXv9OnTOHbsGBwcHDRpDg4O+Pjjj9GxY0ejVo6IiIjI2ERC6VbdMuqDSrfotWzZErdv39ZJz8zMRPPmzY1SKSIiIqIaY0Lr6BkU6CkUCs0WExODiRMnYtOmTUhLS0NaWho2bdqEqKgozJs3r6brS0REREQGMqjr1t7eXuv2ZoIg4JVXXtGkCf+bY9yvXz+oVKoaqCYRERGRkZjQgskGBXp79uyp6XoQERER1Q4ur6Lt2Wefrel6EBEREZGRVXmF4/z8fKSkpKC4uFgrvW3bttWuFBEREVGNYYte+e7cuYPXXnsNP/30k979HKNHRERE/2omFOhVenmVqKgo3Lt3D4cPH4aVlRV27dqFNWvWoEWLFti2bVtN1JGIiIiIqqDSLXq//fYbtm7dio4dO0IsFsPLywu9evWCTCZDbGwsXnjhhZqoJxEREZFxmNCs20q36OXl5cHV1RUA4OjoiDt37gAA2rRpgxMnThi3dkRERERGVnZnjOpu9UGlW/RatmyJixcvwtvbG+3bt8eKFSvg7e2N5cuXw8PDoybqSGRShgWcwZg2p+FilY/LOQ6IORyG47f1v7eC3NIxpeNh+MhzYGWuxK0HdthwIQBrzrbTyjcq8C8M9T8LD9sHuFcoxe7rvvjsWCcUq6o8H4uMoHBLIQrWFUGdrYaZjxlsJlrBor1FufmFYgEFqwtRtLsY6rtqiF3EsBolhfRFSwCA8qoKBV8VQHlRBXWGGtYTrWA1WFpbl0OPeHFUFga9eQeOriW4cUmK5bMa4cyftuXmb9P5AcbPuQUvv0Jk37bAxjgX/Pi1s2a/l18hRk7NQPO2+XD3LMHyWY3w/Vcu2uccmYUXRmbDzbN0kuSNi1J8u8gNx/bIauYiqV6o0hi99PR0AMDs2bOxa9cuNG3aFEuXLkVMTEylyho9ejQGDBiglbZp0yZIpVLMnz8fADBnzhyIRCKdzd/fX3PMc889h6ioKK3HIpEIGzZs0Cp78eLF8Pb21jxOTEzUW7ZUWv4H4969eyESiZCTk6Ozz9vbG4sXL9ZJj4mJgZmZGT755BOtvPrOXbY999xzFeZ7tKzHXb16FUOHDkWjRo0glUrRpEkT9O/fH5cuXdLkebQsOzs7BAcHY8uWLZr9hj7v+vJERERo1WfPnj14/vnn4eTkBGtra7Rq1QqTJ0/GzZs3q/ycNlR9fa5gRqdDiD/1FAb88DKOZ3jgy94/wsPmvt78+UoLfHOuNYb/2B/Pbx6M+FNPISroKF5peU6Tp1+zS5gcfARfnAzG85sHY+bB5/C8zz+YHHykti6L9Cj6pRh5SwpgNVIK+WoZLNqaQzHlAVQZ6nKPuf9BHkqOlcBmhjXs18tg+6ENzLzMHilUgLiRGNZvWkHkVD+6lRqiZ1+6h4gPb2H9UldEhvvhzBEbzP32GlwaF+vN7+ZZhLnfXMOZIzaIDPfDhs9d8eZHt/DM8zmaPJZWaqSnSLAqxgPZt/X/QLuTboFVMR54u68f3u7rh9O/22LO6uvw8iusicus30zoFmiV/jn/6quvav7foUMHXL9+HRcuXEDTpk3h7OxcwZFP9tVXX2HChAlYtmwZxo4dq0kPDAzEL7/8opXX3LziqkulUrz//vsYOHAgLCzK/4Usk8lw8eJFrbRH7wJiDKtXr8a0adOwatUqvPvuuwCAo0ePamYoHzp0CAMHDsTFixchk5X+8pJIJJrjo6Oj8cYbb2iVaWdnp/dcxcXF6NWrF/z9/bFlyxZ4eHggLS0NO3fuRG5urk69+vTpg5ycHHz66acYNGgQDh48iJCQEACGPe9vvPEGoqOjtdKsra01/1+xYgUiIyMxatQobN68Gd7e3khJScHatWvx2WefYeHChRU/eSbmtdZ/YfMlf2y6FAAAiDkShmeapGJowDksPNZJJ//5bGecz374vrv5QIZe3tcQ7J6O7y62AgC0d72NE5nu2HG1hSbPjqvN0dYlsxauiMpTmFQIyxclkL5U2hpnHmWNkj9LUPh9EWzetNLJX3y4BMpTSthvlEEsK/2NbvZYQ695gDnMA0rfo/nxBTV7AVSu/xuXhd3rHbFrnRMAYPnsxgh67j5eHJmN1bG6rfMvjsxG5k0LLJ/dGACQekUKv7YFGBhxBwd32gMALp22xqXTpZ+tr7+Xrve8R5LlWo8T53ngxZHZ8A/Kw41LbNk1VdXut7G2tsZTTz1V7YrMnz8fs2bNwrp16zBw4ECtfebm5nB3d69UeUOHDsX27dvx5ZdfIjIystx8IpGo0mVXxr59+1BQUIDo6GisXbsW+/fvR9euXeHi8rDJ3dHREQDg6uoKe3t7nTLs7OwMruO5c+dw9epV/Pbbb/Dy8gIAeHl5ISwsTCevvb093N3d4e7ujuXLl2PDhg3Ytm2bJtAz5Hm3trYuN09aWhomTpyIiRMnYtGiRZp0b29vdO3aVW8LnimzEKsQ6HwHK//qoJX++80m6OCaYVAZAU5Z6OCagcXHn9akHb/tgZeaXUYb59v4O8sNTewUeNYzBd9fbmnU+pPhhBIByosqSIdrf/laPG0B5Rml3mOKD5bA3N8MBd8WomhXMURWIkiesYD1G1YQWbL17t/C3EKNFm3zkfSFq1b68X12aBWcp/eYgKB8HN+n/eP92F479B6aDTNzASpl5V9fsVhAl345sLRW4/wxm0of39CJUP0xdvXlXWdQoDdp0iSDC6xKC827776LZcuWYceOHejZs2elj9dHJpPhvffeQ3R0NEaNGgUbm7r5Q09ISMDQoUNhYWGBoUOHIiEhAV27dq2x87m4uEAsFmPTpk2IioqCmZnZkw8CYGFhAXNzc5SUlBitLhs3bkRxcTGmTZumd7++oLYyioqKUFRUpHmsUCiqVV5dc5AWwlwsILtAuzUnq8AaLlapFR67b8jXcJQWwEwk4IuTwZoWQQDYebU5HKUFWPfiVohEgIVYjXXnW+HLxwJKqj1CjgCoALGj9ugZkYMI6mz9XbfqWyqU/KUEJCLYxdpCyBGQ91k+BIUA2/f4Rf5vIXNUwcwcyMnS/nrNuWMOB1f9QbyDSwly7mgHejlZ5jC3AOSOStzNLL9X6nHe/gVYvP0KJJZqFOSJET3GGymX2Zpnygwao3fy5EmDtlOnTlW6Aj/99BPmzZuHrVu3lhvk/f3337C1tdXaHu3aLU9kZCSkUmmFwWdubq5O2eHh4U8su0mTJjrHpaSkaOVRKBTYvHkzhg8fDgAYPnw4Nm3aVOmAZPr06Trn2rt3r968jRs3xtKlSzFr1iw4ODige/fu+Oijj3D16tVyyy8qKsLcuXOhUCjQo0cPTbohz3tcXJxOnjVr1gAALl++DJlMZvAkHUOe00fFxsZCLpdrNk9PT4PO82/3+I9MEQQIT/jt+OqO/hi4dSBmH+qCkYF/4QXfy5p9T7vfRES7E/jwUBf83w8DMeGX3njOMwWR7Y/XQO2pUvS9rOW91P+L/2xn28CilTkkoRawftsKRTuLIRTVk8FCJkR47CURiVDhmK7H85f9HeikP0HaP5aI7OWH/77YAjvWOmPKkhQ0bcExejrKllep7lYPGNSit2fPnhqrQNu2bZGVlYVZs2ahY8eOeseetWzZUmcx5vLGqD3K0tIS0dHReOutt/Dmm2/qzWNnZ6ezLIyVle74mMcdOHBApw5lEyjKrFu3Dr6+vmjXrnQGZPv27eHr64sNGzZg3LhxTzxHmalTp2L06NFaaY0bNy43/4QJEzBy5Ejs2bMHR44cwcaNGxETE4Nt27ahV69emnxDhw6FmZkZCgoKIJfLsWDBAvTt21ez35Dn/dVXX8XMmTO10sqW3xEEoVLjHQ15Th81Y8YMrdZmhUJRr4O9e4VSKNUiOFtpj61ysipAVkHFf5NpD0rHdl665wRnqwK83eEYfvzfmLz/Bh3Ftit+mla+S/ecYG1eguhn9iP+1FNPDCLJ+ET2IsAMOq13wj1Bp5WvjNhJDLGLGGLbh6+XmbcZIADqTDXMPA1rvaeapbhrBpUScHDRbr2TOytx747+r9x7dyx0WvvsnZRQlgCKe5UbYaUsEePW9dJxn5f/skbL9vkYMPYOlk6vv5+NNcKE7oxR52srNG7cGJs3b0a3bt3Qp08f7Nq1S+fLXiKRoHnz5lUqf/jw4ViwYAHmzp2rNeO2jFgsrlLZPj4+Ol2Pj09UWLVqFc6ePauVrlarkZCQUKlAz9nZudJ1tLOzw0svvYSXXnoJc+fORe/evTF37lytQG/RokXo2bMnZDKZJjh7lCHPu1wuLzePn58fcnNzkZ6eblCrniHP6aMsLS1haWn5xHLrixK1Gc5muSCscSp+ueGjSQ9tdBO/pngbXI4IgIXZw1sRSs2VUD8WzKkEEUQQIBIJEOrJr9KGRGQhgnlLM5QcVcLy2YcTr0qOlsDiGYneY8zbmqNoTzGEfAEi69LXTJ2qAsSA2LXSCyhQDVGWiHH5L2s81fU+Du16ODniqa738cduud5jzh+3Rqde2j09Qc/ex6XT1lUan/c4C0k9iUioRvwrPh2aNm2Kffv2ITMzE+Hh4UYdayUWixETE4P4+Hhcv37daOU+yd9//41jx45h7969OHXqlGbbv38/jh49ijNnztRaXcqWRcnL0x4I7O7ujubNm+sN8ozh5ZdfhkQi0SyV8zhOxtC1+kxbvOx3AQNbXICv/B5mdPodHrb3seFC6QzaScFHMK/rb5r8wwLOoJvndXjJcuAly8H/tbiA19ucxvZ//DR59qR4Yaj/WTzvewVNbBUIbZSK/wYdxW8p3lAL/4qPAJMkHSxF0fYiFO4ogvK6CnlL8qG6rYb0P6WBXl58Ae5/9PA9a9lLArFchAcxeVBeU6HkVAnylhXA8gWJZjKGUCJAeUkJ5SUlhBIB6jtqKC8poUrjPchr05aVzugz7C7Ch2TDs3khxs+5CdfGJfhxbeks3NdmpGPqkofDUnasdYJbkxKMm30Tns0LET4kG72H3sXm5Q8n7ZlbqOEbWADfwAJYWAhw8iiBb2ABGnk/HKf82rvpaP30A7g1KYa3fwFGT09H29AH2PO9Q+1dfH3B5VVqX5MmTbB3715069YN4eHh2L17N+Ty0l8/SqUSGRnasw5FIhHc3NwMKvvFF19Ep06dsGLFCp1jBEHQKRso7X4Ui6v+JZiQkICnn35a78SLkJAQJCQkaM1Ercj9+/d16mhtba1ZiuVRp06dwuzZszFixAi0atUKEokE+/btw6pVqzB9+vRKXYMhz3t+fr5OHktLSzg4OMDT0xOLFi3CW2+9BYVCgZEjR8Lb2xtpaWlYu3YtbG1t8dlnn1WqTg3dT9eaw0FaiMgOx+BqnY9L9xwx7ufncetBaSu3i1UePGwfrqknFgmY1PEImtjeh0oQI0Uhw2dHO2kCQwCIPxUEASJEBf0JN+s83C20wp4ULyx6ZGYu1T7LnhIICjUKVheWLpjsawbZAluYuZd2wQrZaqhvP+zaFVmLIFtsh7yF+cgdo4BYLoKkuwTW4x5266uz1Mh97eHfR+H6IhSuL4J5B3PIv3jycBcyjn3bHGDnoMKr79yGo6sSNy5K8f5wH2TeLA3iHV1LtNbUu51qifeH+2D8h7fQb3Q27t62QPwHjTRLqwCAk5sS8ckP10Id9OYdDHrzDk4fssG0l0t7VexdlJj6eQocXZXIv2+Ga+eleP9VX5zYz9f+cca4s0VVj4+Li8Onn36K9PR0BAYGYvHixejSpcsTj/v999/x7LPPonXr1pWaE/GvCfSA0m7cffv2oVu3bujVqxd+/vlnAMDZs2d1uv4sLS1RWGj4ANN58+YhNDRUJ12hUOjtVkxPT6/ysivFxcX45ptvyg2sBg4ciNjYWMybN09rvbzyzJo1C7NmzdJKGz9+PJYvX66Tt0mTJvD29saHH36I69evQyQSaR6/8847lboOQ573L7/8El9++aVWnt69e2PXrl0ASifE+Pn5YcGCBfjPf/6DgoICeHt748UXX6zUbG5Tsu58a6w731rvvhkHums9/uZcG3xzrk2F5akEMZadDMayk8FGqyMZh/T/pJD+n/4Zkbbv686kNfMyg2xJ+V/aZh5mcPqdrTf/BjvWOGPHGv1ry372TlOdtL8P2+Kt3n56cpe6nSZB70btyt0PAIsmcxzev11SUhKioqIQFxeHsLAwrFixAn379sW5c+fQtKnu30WZ3NxcjBw5Ej169MDt27crdU6RIFR2Tg/Rv5NCoYBcLofv+x9DXMHdTahhCOp+oa6rQLUoO+xeXVeBaoFSKMFebEVubq7eXqvqKvue8J5b/e8JdWEhrr8/E6mpqVp1rWj8eKdOnfDUU08hPj5ekxYQEIABAwYgNja23HMNGTIELVq0gJmZGX744YdKtehVqW/y66+/RlhYGBo1aoQbN24AKL292NatW6tSHBEREVHtMeIYPU9PT62lvsoL2IqLi3H8+HGdJdzCw8Nx6NChcqu6evVq/PPPP5g9e3aVLrXSgV58fDwmTZqE559/Hjk5OZrbeNnb25vUPUmJiIiIUlNTkZubq9lmzJihN19WVhZUKpXOXAE3Nze9cwWA0vVo3333XXz77bdPvPVreSod6H3++ef48ssvMXPmTK27LgQHB+Pvv/+uUiWIiIiIakvZZIzqbkDpnbge3Z607Nfj68uWt+asSqXCsGHD8OGHH8LPr/zxm09S6fDw2rVr6NBB99ZJlpaWOst3EBEREf3rGOPOFpU83tnZGWZmZjqtd5mZmXpXEbl//z6OHTuGkydP4q233gJQuhavIAgwNzfHzz//jO7du+sc97hKt+j5+PjoHQT4008/oVWrVroHEBEREf2b1ME6ehKJBEFBQUhOTtZKT05O1rsqiEwmw99//621Fm9ERARatmyJU6dOoVOnTgadt9ItelOnTsWECRNQWFgIQRDw559/Yv369YiNjcVXX31V2eKIiIiITMKkSZMwYsQIBAcHIyQkBCtXrkRKSgoiIiIAlN7a8+bNm1i7di3EYjFat9ZeasvV1RVSqVQnvSKVDvRee+01KJVKTJs2Dfn5+Rg2bBgaN26MJUuWYMiQIZUtjoiIiKhW1dWCyYMHD0Z2djaio6ORnp6O1q1bY+fOnfDy8gJQuoZvSkrKE0qpbD2rsY5eVlYW1Gp1jd1Ci6gyuI6eaeE6eqaF6+iZhtpaR893VoxR1tG7Gv1ejdXVWKp1ZwxnZ/2rfhMRERFR3at0oOfj46N3GnCZq1evVqtCRERERDXKCF23lZ2MUVcqHehFRUVpPS4pKcHJkyexa9cuTJ061Vj1IiIiIqoZVZg1q7eMeqDSgd5///tfvenLli3DsWPHql0hIiIiIjKOKt3rVp++ffti8+bNxiqOiIiIqGbUwTp6daVakzEetWnTJjg6OhqrOCIiIqIaUVfLq9SFSgd6HTp00JqMIQgCMjIycOfOHcTFxRm1ckRERERUdZUO9AYMGKD1WCwWw8XFBc899xz8/f2NVS8iIiIiqqZKBXpKpRLe3t7o3bs33N3da6pORERERDXHhGbdVmoyhrm5Od58800UFRXVVH2IiIiIalTZGL3qbvVBpWfddurUCSdPnqyJuhARERGREVV6jF5kZCQmT56MtLQ0BAUFwcbGRmt/27ZtjVY5IiIiohpRT1rkqsvgQO/111/H4sWLMXjwYADAxIkTNftEIhEEQYBIJIJKpTJ+LYmIiIiMxYTG6Bkc6K1ZswaffPIJrl27VpP1ISIiIiIjMTjQE4TS0NXLy6vGKkNERERU07hgcjkeXSiZiIiIqF5i161+fn5+Twz27t69W60KEREREZFxVCrQ+/DDDyGXy2uqLkREREQ1jl235RgyZAhcXV1rqi5ERERENc+Eum4NXjCZ4/OIiIiI6pdKz7olIiIiqtdMqEXP4EBPrVbXZD2IiIiIagXH6BERERE1VCbUomfwGD0iIiIiql/YokdERESmxYRa9BjoERERkUkxpTF67LolIiIiaqDYokdERESmhV23RERERA0Tu26JiIiIqN5jix4RERGZFnbdEhERETVQJhToseuWiIiIqIFiix4RERGZFNH/tuqWUR+wRY+IiIhMi2CkrQri4uLg4+MDqVSKoKAgHDhwoNy8Bw8eRFhYGJycnGBlZQV/f38sWrSoUudjix4RERGZlLpaXiUpKQlRUVGIi4tDWFgYVqxYgb59++LcuXNo2rSpTn4bGxu89dZbaNu2LWxsbHDw4EGMHz8eNjY2GDdunEHnZIseERERUS1YuHAhxowZg7FjxyIgIACLFy+Gp6cn4uPj9ebv0KEDhg4disDAQHh7e2P48OHo3bt3ha2Aj2OgR0RERKbFiF23CoVCaysqKtJ7yuLiYhw/fhzh4eFa6eHh4Th06JBB1T558iQOHTqEZ5991uBLZaBHREREpsdI4/M8PT0hl8s1W2xsrN7TZWVlQaVSwc3NTSvdzc0NGRkZFVa1SZMmsLS0RHBwMCZMmICxY8cafJkco0dERERURampqZDJZJrHlpaWFeYXibTn6wqCoJP2uAMHDuDBgwc4fPgw3n33XTRv3hxDhw41qH4M9IiIiMikGHMyhkwm0wr0yuPs7AwzMzOd1rvMzEydVr7H+fj4AADatGmD27dvY86cOQYHeuy6JSIiItNSB8urSCQSBAUFITk5WSs9OTkZoaGhhlddEModB6gPW/SIiIiIasGkSZMwYsQIBAcHIyQkBCtXrkRKSgoiIiIAADNmzMDNmzexdu1aAMCyZcvQtGlT+Pv7AyhdV2/BggV4++23DT4nAz0iIiIyKXW1jt7gwYORnZ2N6OhopKeno3Xr1ti5cye8vLwAAOnp6UhJSdHkV6vVmDFjBq5duwZzc3M0a9YMn3zyCcaPH2/wORnoERERkWmpxp0ttMqogsjISERGRurdl5iYqPX47bffrlTrnT4co0dERETUQLFFjxqcl/ochqWtRV1Xg2rYgeiQuq4C1SKbDg/qugpUC0SqIuD01po/Tx113dYFBnpERERkWuqw67a2MdAjIiIi02JCgR7H6BERERE1UGzRIyIiIpPCMXpEREREDRW7bomIiIiovmOLHhEREZkUkSBAJFSvSa66x9cWBnpERERkWth1S0RERET1HVv0iIiIyKRw1i0RERFRQ8WuWyIiIiKq79iiR0RERCaFXbdEREREDZUJdd0y0CMiIiKTYkotehyjR0RERNRAsUWPiIiITAu7bomIiIgarvrS9Vpd7LolIiIiaqDYokdERESmRRBKt+qWUQ8w0CMiIiKTwlm3RERERFTvsUWPiIiITAtn3RIRERE1TCJ16VbdMuoDdt0SERERNVBs0SMiIiLTwq5bIiIioobJlGbdMtAjIiIi02JC6+hxjB4RERFRA8UWPSIiIjIp7LolIiIiaqhMaDIGu26JiIiIGii26BEREZFJMaWuW7boERERkWkpm3Vb3a0K4uLi4OPjA6lUiqCgIBw4cKDcvFu2bEGvXr3g4uICmUyGkJAQ7N69u1LnY6BHREREVAuSkpIQFRWFmTNn4uTJk+jSpQv69u2LlJQUvfn379+PXr16YefOnTh+/Di6deuGfv364eTJkwafk123REREZFLqqut24cKFGDNmDMaOHQsAWLx4MXbv3o34+HjExsbq5F+8eLHW45iYGGzduhXbt29Hhw4dDDonW/SIiIjItAhG2gAoFAqtraioSO8pi4uLcfz4cYSHh2ulh4eH49ChQwZVW61W4/79+3B0dDT4UhnoEREREVWRp6cn5HK5ZtPXMgcAWVlZUKlUcHNz00p3c3NDRkaGQef67LPPkJeXh1deecXg+rHrloiIiEyKMbtuU1NTIZPJNOmWlpYVHycSaT0WBEEnTZ/169djzpw52Lp1K1xdXQ2uJwM9IiIiMi1qoXSrbhkAZDKZVqBXHmdnZ5iZmem03mVmZuq08j0uKSkJY8aMwcaNG9GzZ89KVZNdt0RERGRajDhGz1ASiQRBQUFITk7WSk9OTkZoaGi5x61fvx6jR4/GunXr8MILL1TupGCLHhEREVGtmDRpEkaMGIHg4GCEhIRg5cqVSElJQUREBABgxowZuHnzJtauXQugNMgbOXIklixZgs6dO2taA62srCCXyw06JwM9IiIiMikiGGGMXhWOGTx4MLKzsxEdHY309HS0bt0aO3fuhJeXFwAgPT1da029FStWQKlUYsKECZgwYYImfdSoUUhMTDTonAz0iIiIyLRU484WWmVUQWRkJCIjI/Xuezx427t3b5XO8SiO0SMiIiJqoNiiR0RERCalru6MURcY6BEREZFpqcKsWb1l1APsuiUiIiJqoNiiR0RERCZFJAgQVXMyRnWPry0M9IiIiMi0qP+3VbeMeoBdt0REREQNFFv0iIiIyKSw65aIiIiooTKhWbcM9IiIiMi01OGdMWobx+gRERERNVBs0SMiIiKTwjtjEFGdyfwOuL1GhJIswKoZ0GSKALunys+vLgbSV4pw90egJBuwcAM8xghwHlC6P2sbcGO2buN9h8NqiC1r5hrIMAO6nMXQnn/BSZ6P6+kOWLopBH/946E3b9d21zCgyzm0aJINC3MVrqU7YPXOIPx53lOTp1/oefTudBm+je4CAC6muGDlto44f8O1Vq6HyvfiC5fw8v+dh6NjAW6kyLF8ZRDOntX/ujg6FOCNsSfQovldNGp0H1u3tcSKL4O08oSFpmLwK2fRyOM+zM3VuHnLDlu2BODXPT61cTn1nwl13TLQI/oXubsbSPtUhKYzBNi0B7I2i3DlLRECNwuQ6P/+x9VpIijvAl6zBVg2BZR3AUGpnUdsK6D199ofSgzy6lb3p/7BxJf/wMKkZ/D3P2546Znz+HTCTxjx0SvIvGerk79d83Qcu9AYK7d1xIMCSzwfchGfROzG+E8H4HKaMwCgvV86fjnWDGeuhaK4xBzDep3CZ2/txMi5g5CVa1Pbl0j/07XLDYx/4wSWxQXj7HkXPN/nCuZ+uBfj3nwBd+7ovi4WFirk5lpifVIg/jPgot4y79+XYENSIFLTZFCWiPH00zcx6Z3DyMm1xPETjWr6kqge4Rg9PUQiUYXb6NGjNXl37NiB5557DnZ2drC2tkbHjh2RmJio2X/69GlYWlpi27ZtWufYvHkzpFIpzpw5AwCYM2cO2rdvr5VHoVBg5syZ8Pf3h1Qqhbu7O3r27IktW7ZAKOeXhEqlQmxsLPz9/WFlZQVHR0d07twZq1ev1uQZPXq05losLCzg6+uLKVOmIC8vDwBw/fr1cq/98OHDAIDExES9+6VSqVZ9MjIy8Pbbb8PX1xeWlpbw9PREv3798Ouvv2ryeHt7Y/HixTrXou85aehufyOC0wDA+f8AK1/Ac6oAiTtwZ6NIb/7c34EHx4HmnwuQdQYsGwE2rQHb9tr5RAAsnLU3qluDe/yFH/9oiR2H/HHjtgM+3xyKzHu2+E+Xc3rzf745FOt+aY8LKa5IuyPHym1PI+2OHGFtbmjyfJTYHT8cCMSVNGek3LbH/G+7QiwSENTyZm1dFunxf/+5gN0/+2LXz82RmirHii+DcCfLGi8+f1lv/tuZtli+Mhi//uaL/DwLvXn++tsNh/7wRGqqHOkZdti6zR/XrtkjsNWdmryUBkOkNs5WH7BFT4/09HTN/5OSkjBr1ixcvPjwV5WVlRUA4PPPP0dUVBSmT5+OuLg4SCQSbN26FREREThz5gwWLFiAdu3a4YMPPsC4ceMQFhYGJycnZGZmIiIiAh9++CFat26ttw45OTl45plnkJubi7lz56Jjx44wNzfHvn37MG3aNHTv3h329vY6x82ZMwcrV67EF198geDgYCgUChw7dgz37t3TytenTx+sXr0aJSUlOHDgAMaOHYu8vDzEx8dr8vzyyy8IDAzUOs7JyUnzf5lMpvW8AKVBcpnr168jLCwM9vb2mD9/Ptq2bYuSkhLs3r0bEyZMwIULF8p7CUySugTIPw+4v6YdxMs6Aw9O6z8md58I1q2AjMTSrluxFWD/LNAoUoD4kZhbVQD83VcEQQ1Ytyzdb+1fgxdDFTI3U8HPMwvf/NxeK/3o+SZo7XvboDJEIgHWlsVQ5JffNGspUcLcTI37FeShmmVurkKL5nfx3cZWWuknTrgjICDLSGcR0L7dbTRpokDC6vZGKrOBY9etaXN3d9f8Xy6XQyQSaaUBQGpqKiZPnoyoqCjExMRo0idPngyJRIKJEydi0KBB6NSpE2bMmIFt27ZhwoQJ2LBhA8aPH48WLVpgypQp5dbhvffew/Xr13Hp0iU0avSwGd7Pzw9Dhw7VaTkrs337dkRGRmLQoEGatHbt2unks7S01FzTsGHDsGfPHvzwww9agZ6Tk5POdT9K3/PyqMjISIhEIvz555+wsXnYPREYGIjXX3+93OMMVVRUhKKiIs1jhUJR7TLrkvIeAJUIFo7aHx7mTgJKsvW36BXdBB6cKu2GbbZQgPIekBIrglIhgvec0nKk3oD3hwKsmgOqPCBznQgXXhOh1QYBUq+avSbST25bCHMzAfcUVlrp9+5bwVGWb1AZQ3r8BalEid+ONys3T0T/P3En1wbHLjSuVn2p6mSyIpiZCbiXo/2ZfS/HCo4O6eUcZRhr62J8u/YHWFiooFaL8EVcR5w8Vc4YDzJZ7Lqtok2bNqGkpERvsDZ+/HjY2tpi/fr1AAAzMzOsWbMGW7duxbBhw7B7924kJibCzMxMb9lqtRobNmzAq6++qhXklbG1tYW5uf4Y3d3dHb/99hvu3Klc872VlRVKSkoqdUxF7t69i127dmHChAlaQV4Zfa2RlRUbGwu5XK7ZPD09n3xQffB4TCfoSSvbpS7d5/OxAJvWgLwL0GSygOxtgLqwNI9tW8DphdKWPLunAN/5AqRNgcwN5RRKtUbQ82Ib0kjQI+gKXnv+OGav6oGcB1Z68wzreQo9g//B+yt7oVjJ3/R17rHXVSQSIAjVew8WFFgg8u2+mPhOHySubYdxY0+gbRvDWoRNnmCkrR5goFdFly5dglwuh4eH7q8niUQCX19fXLp0SZMWEBCAqKgorF+/HnPmzIGfn1+5ZWdlZeHevXvw969839rChQtx584duLu7o23btoiIiMBPP/1U4TF//vkn1q1bhx49emilh4aGwtbWVmtTqVSa/bm5uTr7w8PDAQBXrlyBIAgGX8P06dN1ynq0pVSfGTNmIDc3V7OlpqYadK5/K3MHAGYCSrK105V3RbBw1H+MhTMgcQXM7B6mSX0ACCIUl/N5LxIDNoFAUYoxak1VkftACqVKpNN652BXiHv3rSs8tvtT/+Dd4fswO6Enjl9sojfPkB6nMbz3KUz64nn8c8tJbx6qHQqFJVQqERwcCrXS7eWFOq18lSUIIqSn2+HqVQds+T4AB39visGDzlarTFNRdgu06m71AX/m1RBBELTGqz148ABJSUmwtrbGgQMHMG3atAqPBbTHuxmqVatWOHPmDI4fP46DBw9i//796NevH0aPHo2vvvpKk2/Hjh2wtbWFUqlESUkJ+vfvj88//1yrrKSkJAQEBGilPdoKaWdnhxMnTmjtLxu/WNlrmDp1qtYkFwBYunQp9u/fX+4xlpaWsLRsOGOPxBaAdQBw/7AIDt0ffoAoDgP2z+k/xra9gHu/iKDKB8z+Fx8U3QAgFiBx03+MIAD5FwGrFkatPlWCUmWGS6nO6Oh/EwdOP1wOo6N/Gg7+5V3ucT2CrmDG8H34cHUP/HG2qd48Q3uexsg+JzD5i+dxMcXF2FWnSlIqzXD5iiM6dMjAoT8e9jp06JCBw4f1B+pVJRIJsLCoJzMEqNYw0KsiPz8/5Obm4tatWzrdq8XFxbh69Sq6d++uSZs6dSokEgkOHTqEkJAQrF27FiNHjtRbtouLCxwcHHD+/Pkq1U0sFqNjx47o2LEj3nnnHXzzzTcYMWIEZs6cCR+f0i+Vbt26IT4+HhYWFmjUqBEsLHRndnl6eqJ58+YVnqe8/S1atIBIJML58+cxYMCAJ9bZ2dlZpyxHx3KasRowt+ECrr9fOsHCpi2QtUWE4gzA+eXSwO/mUhGKMwGfuaWPHfsC6V8C12eL0ChCgDIHSFssgnN/aCZj3FoB2LQBpE3/N0ZvvQj5l4CmM+rHr9GGKunXtnh/1B5cSHHG2auly6u4Oj7ADwdLf1yNf+lPONvn4eO13QCUBnnvj9qDJRtDcfa6q6Y1sKjYHHmFEgCl3bVjXjyG6MTuyLhrp8lTUGSBgiL9szep5m353h9TJ/+By5cdcf6CM/r2uQJXl3z8uLP019Zro07BySkfCxaGao7x9S2dQCe1UkIuL4Sv7z0oS8RISZUDAAYPOotLlx2RnmEHc3MVng6+hR7dr+GLZR1r/wLrI07GoCcZOHAgpk2bhs8++wyfffaZ1r7ly5cjLy8PQ4cOBQAkJyfjq6++woEDB9CuXTvExMQgKioKvXr10tv1KxaLMXjwYHz99deYPXu2TiCZl5cHS0vLcsfpPa5Vq1aa48rY2NhUGMRVl6OjI3r37o1ly5Zh4sSJOuP0cnJyjDJOr6Fx7A0ocwWkr/zfgsnNS5dOsfzfn0BJFlCc8TC/mTXgFy8gZZ4I54eLYC4HHHoBjSc8/ABS3Rch5aPSxZTNbAFrf6DlV6Vj+qju/HaiGWQ2hRjd9wScZPm4lu6IaXF9cftuaT+8kzwfbg4PNPn7P3Me5mYCJg/5HZOH/K5J/+mwH2K+fg4AMKDrOUgs1Jj7xi9a51r141NYvTO45i+K9Np/wAsyWRFeHXoGDo4FuHFDjg9mP4fM/62h5+hYAFcX7W78uM8fDrnxa3EX3bvdwO3bNhj1en8AgFSqxFuRR+HsXIDiYjOkpskwf0Eo9h/gDCuDCACq2/hZP+I8BnpV1bRpU8yfPx9TpkyBVCrFiBEjYGFhga1bt+K9997D5MmT0alTJygUCowZMwZTpkxB586dAQATJ07E5s2bMW7cOGzfvl1v+TExMdi7dy86deqEjz/+GMHBwbCwsMCBAwcQGxuLo0eP6g2UXn75ZYSFhSE0NBTu7u64du0aZsyYAT8/v0qP+cvOzkZGRoZWmr29vWbGryAIOvsBwNXVFWKxGHFxcQgNDcXTTz+N6OhotG3bFkqlEsnJyYiPj69yi2VD5/oK4PqK/k8Q72jddKkP4Le8/E8czykCPMuf4E116IcDgfjhQKDefWXBW5mJS/o9sbxXZg0zRrWoBuz40Q87ftQ/NvuzRSE6aX1eqPi1XPN1O6z5WndFBTKMMcbYcYyeCXjnnXfQrFkzLFiwAEuWLIFKpUJgYCDi4+Px2muvAQCioqIgl8vx4Ycfao4Ti8VYvXo12rVrV24XroODAw4fPoxPPvkEc+fOxY0bN+Dg4IA2bdrg008/hVwu11un3r17Y/369YiNjUVubi7c3d3RvXt3zJkzx+AWwDI9e/bUSVu/fj2GDBkCoHQ5E30tkunp6XB3d4ePjw9OnDiBjz/+GJMnT0Z6ejpcXFwQFBSktYwLERER1QyRUN4tFojqGYVCAblcjqiD/WBpy/FIDd2BaN1WEGq4bK4/eHImqveUqiLsOf0JcnNzIZPJjF5+2fdE9/bvwtysepP5lKoi/Haq5upqLGzRIyIiItNiQpMxuI4eERERUQPFFj0iIiIyLf+7q1C1y6gHGOgRERGRSTGlWbfsuiUiIiJqoNiiR0RERKbFhCZjMNAjIiIi02JCgR67bomIiIgaKLboERERkWkxoRY9BnpERERkWkxoeRV23RIREZFJKVtepbpbVcTFxcHHxwdSqRRBQUE4cOBAuXnT09MxbNgwtGzZEmKxGFFRUZU+HwM9IiIiolqQlJSEqKgozJw5EydPnkSXLl3Qt29fpKSk6M1fVFQEFxcXzJw5E+3atavSORnoERERkWkpG6NX3a2SFi5ciDFjxmDs2LEICAjA4sWL4enpifj4eL35vb29sWTJEowcORJyubxKl8oxekRERGRa1AIgquZkCnXp8QqFQivZ0tISlpaWOtmLi4tx/PhxvPvuu1rp4eHhOHToUPXqUgG26BERERFVkaenJ+RyuWaLjY3Vmy8rKwsqlQpubm5a6W5ubsjIyKix+rFFj4iIiEyLEZdXSU1NhUwm0yTra817lEikPd1XEASdNGNioEdEREQmxgiBHkqPl8lkWoFeeZydnWFmZqbTepeZmanTymdM7LolIiIiqmESiQRBQUFITk7WSk9OTkZoaGiNnZctekRERGRa6ujOGJMmTcKIESMQHByMkJAQrFy5EikpKYiIiAAAzJgxAzdv3sTatWs1x5w6dQoA8ODBA9y5cwenTp2CRCJBq1atDDonAz0iIiIyLWoBZV2v1SujcgYPHozs7GxER0cjPT0drVu3xs6dO+Hl5QWgdIHkx9fU69Chg+b/x48fx7p16+Dl5YXr168bdE4GekRERES1JDIyEpGRkXr3JSYm6qQJ1Wx5ZKBHREREpkVQl27VLaMeYKBHREREpqWOxujVBQZ6REREZFrqaIxeXeDyKkREREQNFFv0iIiIyLSw65aIiIiogRJghEDPKDWpcey6JSIiImqg2KJHREREpoVdt0REREQNlFoNoJrr4Knrxzp67LolIiIiaqDYokdERESmhV23RERERA2UCQV67LolIiIiaqDYokdERESmxYRugcZAj4iIiEyKIKghCNWbNVvd42sLAz0iIiIyLYJQ/RY5jtEjIiIiorrEFj0iIiIyLYIRxujVkxY9BnpERERkWtRqQFTNMXb1ZIweu26JiIiIGii26BEREZFpYdctERERUcMkqNUQqtl1W1+WV2HXLREREVEDxRY9IiIiMi3suiUiIiJqoNQCIDKNQI9dt0REREQNFFv0iIiIyLQIAoDqrqNXP1r0GOgRERGRSRHUAoRqdt0KDPSIiIiI/oUENarfosflVYiIiIioDrFFj4iIiEwKu26JiIiIGioT6rploEcNRtmvq6K8kjquCdUGZUlhXVeBapFSVVTXVaBaUPY613RrmRIl1V4vWYn68V0jEupL2yPRE6SlpcHT07Ouq0FERNWUmpqKJk2aGL3cwsJC+Pj4ICMjwyjlubu749q1a5BKpUYpryYw0KMGQ61W49atW7Czs4NIJKrr6tQahUIBT09PpKamQiaT1XV1qAbxtTYdpvpaC4KA+/fvo1GjRhCLa2a+aGFhIYqLi41SlkQi+VcHeQC7bqkBEYvFNfILsL6QyWQm9YVgyvhamw5TfK3lcnmNli+VSv/1wZkxcXkVIiIiogaKgR4RERFRA8VAj6ies7S0xOzZs2FpaVnXVaEaxtfadPC1JmPhZAwiIiKiBootekREREQNFAM9IiIiogaKgR4RERFRA8VAj4iIiKiBYqBH9D+jR4/GgAEDdNL37t0LkUiEnJwcnX0tW7aERCLBzZs3tfJWtCUmJlaYr6Jb82zevBmdOnWCXC6HnZ0dAgMDMXnyZM3+xMRErbI8PDzwyiuv4Nq1a5o83t7ees/7ySefAACuX79ebt0OHz6sKae4uBjz589Hu3btYG1tDWdnZ4SFhWH16tUoKSmp8nNa0euxadMmSKVSzJ8/HwAwZ84cvfX09/fXHPPcc88hKipK67FIJMKGDRu0yl68eDG8vb3LfS7LtooWWq3oury9vbF48WKd9JiYGJiZmWme/7K8Ff0NPffccxXme7Ssx129ehVDhw5Fo0aNIJVK0aRJE/Tv3x+XLl3S5Hm0LDs7OwQHB2PLli2a/YY+7/ryREREaNVnz549eP755+Hk5ARra2u0atUKkydP1nlPGfqcPun9N3r0aE3eHTt24LnnnoOdnR2sra3RsWNHJCYmavafPn0alpaW2LZtm9Y5Nm/eDKlUijNnzmiej/bt22vlUSgUmDlzJvz9/SGVSuHu7o6ePXtiy5Yt5d7HVaVSITY2Fv7+/rCysoKjoyM6d+6M1atXa/KMHj1acy0WFhbw9fXFlClTkJeXB8Cw96+hf9sZGRl4++234evrC0tLS3h6eqJfv3749ddfK3wNyntOqG7wzhhEVXTw4EEUFhZi0KBBSExMxMyZMxEaGor09HRNnv/+979QKBRaH9RyuRxHjhwBAFy8eFFn1XtXV1e95/vll18wZMgQxMTE4KWXXoJIJMK5c+e0PnSB0pX0L168CEEQcOHCBYwfPx4vvfQSTp06BTMzMwBAdHQ03njjDa3j7OzsdM4XGBiolebk5ASgNMjr3bs3Tp8+jY8++ghhYWGQyWQ4fPgwFixYgA4dOhj9Q/6rr77ChAkTsGzZMowdO1aTHhgYiF9++UUrr7l5xR9tUqkU77//PgYOHAgLC4ty85U9l48y9u31Vq9ejWnTpmHVqlV49913AQBHjx6FSqUCABw6dAgDBw7U+luRSCSa4w15LcsUFxejV69e8Pf3x5YtW+Dh4YG0tDTs3LkTubm5OvXq06cPcnJy8Omnn2LQoEE4ePAgQkJCABj2vL/xxhuIjo7WSrO2ttb8f8WKFYiMjMSoUaOwefNmeHt7IyUlBWvXrsVnn32GhQsXVvzk6fHo+y8pKQmzZs3Seg2trKwAAJ9//jmioqIwffp0xMXFQSKRYOvWrYiIiMCZM2ewYMECtGvXDh988AHGjRuHsLAwODk5ITMzExEREfjwww/RunVrvXXIycnBM888g9zcXMydOxcdO3aEubk59u3bh2nTpqF79+6wt7fXOW7OnDlYuXIlvvjiCwQHB0OhUODYsWO4d++eVr4+ffpoflAdOHAAY8eORV5eHuLj4zV5Knr/Ak/+275+/TrCwsJgb2+P+fPno23btigpKcHu3bsxYcIEXLhwobyXgP5lGOgRVVFCQgKGDRuGZ599FhMmTMB7770HiUQCd3d3TR4rKysUFRVppT3K1dVV7we+Pjt27MAzzzyDqVOnatL8/Px0Wr1EIpHmfB4eHpg9ezaGDx+OK1euoGXLlgBKA4Hy6lTGycmp3DyLFy/G/v37cezYMXTo0EGT7uvri0GDBhntPpJl5s+fj1mzZmHdunUYOHCg1j5zc/MnXsvjhg4diu3bt+PLL79EZGRkufkefS5rwr59+1BQUIDo6GisXbsW+/fvR9euXeHi4qLJ4+joCKD8vxVDXssy586dw9WrV/Hbb7/By8sLAODl5YWwsDCdvPb29nB3d4e7uzuWL1+ODRs2YNu2bZpAz5Dn3drautw8aWlpmDhxIiZOnIhFixZp0r29vdG1a9cKW3sr8uj55HK53tcwNTUVkydPRlRUFGJiYjTpkydPhkQiwcSJEzFo0CB06tQJM2bMwLZt2zBhwgRs2LAB48ePR4sWLTBlypRy6/Dee+/h+vXruHTpEho1aqRJ9/Pzw9ChQ8ttFd6+fTsiIyMxaNAgTVq7du108llaWmquadiwYdizZw9++OEHrUCvovcv8OS/7cjISIhEIvz555+wsbHRpAcGBuL1118v9zj692HXLVEV3L9/Hxs3bsTw4cPRq1cv5OXlYe/evTV6Tnd3d5w9e1bTXWSoshaMsu5UY/j222/Rs2dPrSCvjIWFhdYXQ3W9++67+Oijj7Bjxw6dIK+qZDIZ3nvvPURHR2u6vOpCQkIChg4dCgsLCwwdOhQJCQk1ej4XFxeIxWJs2rRJ02JoCAsLC5ibmxv1b2jjxo0oLi7GtGnT9O439AdQVWzatAklJSV6g7Xx48fD1tYW69evBwCYmZlhzZo12Lp1K4YNG4bdu3cjMTFR0zr+OLVajQ0bNuDVV1/VCvLK2Nraltvi7O7ujt9++w137typ1PVYWVkZ9bW5e/cudu3ahQkTJuh9L9fka0PGx0CP6BE7duyAra2t1ta3b1+dfBs2bECLFi0QGBgIMzMzDBkypEpf0k2aNNE6V1mLmz5vv/02OnbsiDZt2sDb2xtDhgzBqlWrUFRUVO4xaWlp+PTTT9GkSRP4+flp0qdPn65znY8HqqGhoTp5yoKDy5cva43Hqoihz6k+P/30E+bNm4etW7eiZ8+eevP8/fffOuU/2rVbnsjISEil0gq7B3Nzc3XKDg8Pf2LZj7+utra2SElJ0cqjUCiwefNmDB8+HAAwfPhwbNq0CQqF4onlP8qQ17JM48aNsXTpUsyaNQsODg7o3r07PvroI1y9erXc8ouKijB37lwoFAr06NFDk27I8x4XF6eTZ82aNQBK/4ZkMhk8PDwMuk5DnlNDXbp0CXK5XO+5JRIJfH19tcYsBgQEICoqCuvXr8ecOXO03kuPy8rKwr179wx+fzxq4cKFuHPnDtzd3dG2bVtERETgp59+qvCYP//8E+vWrdN6bYCK379AxX/bV65cgSAIBl+Dvr/BR1tKqW6x65boEd26ddPq/gCAI0eOaL6MyyQkJGilDR8+XNPdVJlfuwcOHNAaT1XR2DIbGxv8+OOP+Oeff7Bnzx4cPnwYkydPxpIlS/DHH39oxj6VfYALgoD8/Hw89dRT2LJli9a4rqlTp2oNSgdKg4BHJSUlISAgQCutrBVDEASDx6oZ+pzq07ZtW2RlZWHWrFno2LGj3rFnLVu21BksX94YtUdZWloiOjoab731Ft588029eezs7HDixAmttLIW0oo8/roC0EygKLNu3Tr4+vpquubat28PX19fbNiwAePGjXviOcoY8lo+asKECRg5ciT27NmDI0eOYOPGjYiJicG2bdvQq1cvTb6hQ4fCzMwMBQUFkMvlWLBggVaAbsjz/uqrr2LmzJlaaWVjUCvzNwQY9pway+N1e/DgAZKSkmBtbY0DBw6U2wpZdixQtbGcrVq1wpkzZ3D8+HEcPHgQ+/fvR79+/TB69Gh89dVXmnxlP56USiVKSkrQv39/fP7551plVfT+BSr+267sNej7G1y6dCn2799v0PFUsxjoET3CxsYGzZs310pLS0vTenzu3DkcOXIER48exfTp0zXpKpUK69evLzdo0MfHx6fS3SDNmjVDs2bNMHbsWMycORN+fn5ISkrCa6+9BuDhB7hYLIabm5verhdnZ2ed63ycp6dnuXn8/Pxw/vx5g+pryHNansaNG2Pz5s3o1q0b+vTpg127dul82UskkideS3mGDx+OBQsWYO7cuVozbsuIxeIqla3vdX08iF+1ahXOnj2rla5Wq5GQkFCpQM+Q1/JxdnZ2eOmll/DSSy9h7ty56N27N+bOnasV6C1atAg9e/aETCbTO0HIkOddLpdX+DeUm5uL9PR0g1r1DHlODVV27lu3bul0rxYXF+Pq1avo3r27Jm3q1KmQSCQ4dOgQQkJCsHbtWowcOVJv2S4uLnBwcDD4/fE4sViMjh07omPHjnjnnXfwzTffYMSIEZg5cyZ8fHwAPPzxZGFhgUaNGumdUFTR+7fsPOXtb9GiBUQiEc6fP6931vzj9P0Nlo0tpbrHrluiSkpISEDXrl1x+vRpnDp1SrNNmzatxsdYPc7b2xvW1tZa48zKPsB9fX2NOlbuUcOGDcMvv/yCkydP6uxTKpVGHffWtGlT7Nu3D5mZmQgPD69012ZFxGIxYmJiEB8fj+vXrxut3Cf5+++/cezYMezdu1frb2j//v04evRopcdhVkfZsiiPv2bu7u5o3rx5ubPAq+vll1+GRCLRLJXzuKpOxjDEwIEDYW5ujs8++0xn3/Lly5GXl4ehQ4cCAJKTk/HVV18hMTER7dq1Q0xMDKKiorRm9z5KLBZj8ODB+Pbbb3Hr1i2d/Xl5eVAqlQbXtVWrVprjypT9ePLy8qpw1nhVOTo6onfv3li2bJne93JNvjZkfGzRI6qEkpISfP3114iOjtZZWmHs2LGYP38+Tp8+rXemnD6ZmZkoLCzUSnNyctL74T1nzhzk5+fj+eefh5eXF3JycrB06VKUlJRotcQY4v79+zrr9VlbW2st9ZKdna2Tx97eHlKpFFFRUfjxxx/Ro0cPfPTRR3jmmWdgZ2eHY8eOYd68eUhISDDq8ipNmjTB3r170a1bN4SHh2P37t2Qy+UASgPLx+spEong5uZmUNkvvvgiOnXqhBUrVugcIwiC3nUNXV1dIRZX/XdyQkICnn76aXTt2lVnX0hICBISErRmolbEkNeyzKlTpzB79myMGDECrVq1gkQiwb59+7Bq1Sqt1mlDGPK85+fn6+SxtLSEg4MDPD09sWjRIrz11ltQKBQYOXIkvL29kZaWhrVr18LW1lZvIGYMTZs2xfz58zFlyhRIpVKMGDECFhYW2Lp1K9577z1MnjwZnTp1gkKhwJgxYzBlyhR07twZADBx4kRs3rwZ48aNw/bt2/WWHxMTg71796JTp074+OOPERwcDAsLCxw4cACxsbE4evSo3pb8l19+GWFhYQgNDYW7uzuuXbuGGTNmwM/Pr9Jj/ip6/wJP/tuOi4tDaGgonn76aURHR6Nt27ZQKpVITk5GfHx8lVssqQ4IRCQIgiCMGjVK6N+/v076nj17BADCvXv3hE2bNglisVjIyMjQW0abNm2Et99+2+Ay9W1//PGH3rJ/++03YeDAgYKnp6cgkUgENzc3oU+fPsKBAwc0eVavXi3I5fIKr9PLy0vvecePHy8IgiBcu3at3LqtX79eU05hYaEQGxsrtGnTRpBKpYKjo6MQFhYmJCYmCiUlJQY/p+XRd+ytW7eEli1bCh07dhTu3bsnzJ49W289LS0tNcc8++yzwn//+99yHwuCIBw6dEgAIHh5eWk9l+U9D+np6XrrXNF1eXl5CYsWLRKKiooEJycnYf78+XrL+OyzzwRnZ2ehqKjIoDIrei0fd+fOHWHixIlC69atBVtbW8HOzk5o06aNsGDBAkGlUmnyARC+//57vWUIgmDw864vT+/evbXKSk5OFnr37i04ODgIUqlU8Pf3F6ZMmSLcunXL4Oe0PE96P2zdulXo0qWLYGNjI0ilUiEoKEhYtWqVZv9rr70mtG7dWvNalLl8+bJgbW0trFmzRvN8tGvXTitPTk6O8O677wotWrTQvF979uwpfP/994JardZbn5UrVwrdunUTXFxcBIlEIjRt2lQYPXq0cP36dU2e8t5TZQx5/xr6t33r1i1hwoQJgpeXlyCRSITGjRsLL730krBnzx5NnvJeA33PCdUNkSCUs0Q3EREREdVrHKNHRERE1EAx0CMiIiJqoBjoERERETVQDPSIiIiIGigGekREREQNFAM9IiIiogaKgR4RERFRA8VAj4iIiKiBYqBHRGREc+bM0br92+jRow26MbyxXb9+HSKRCKdOnSo3j7e3NxYvXmxwmYmJiXpv3VVZIpEIP/zwQ7XLIaInY6BHRA3e6NGjIRKJIBKJYGFhAV9fX0yZMkXvDduNbcmSJUhMTDQoryHBGRFRZZjXdQWIiGpDnz59sHr1apSUlODAgQMYO3Ys8vLyEB8fr5O3pKQEFhYWRjmvXC43SjlERFXBFj0iMgmWlpZwd3eHp6cnhg0bhldffVXTfVjW3bpq1Sr4+vrC0tISgiAgNzcX48aNg6urK2QyGbp3747Tp09rlfvJJ5/Azc0NdnZ2GDNmDAoLC7X2P951q1arMW/ePDRv3hyWlpZo2rQpPv74YwCAj48PAKBDhw4QiUR47rnnNMetXr0aAQEBkEql8Pf3R1xcnNZ5/vzzT3To0AFSqRTBwcE4efJkpZ+jhQsXok2bNrCxsYGnpyciIyPx4MEDnXw//PAD/Pz8IJVK0atXL6Smpmrt3759O4KCgiCVSuHr64sPP/wQSqWy0vUhoupjoEdEJsnKygolJSWax1euXMF3332HzZs3a7pOX3jhBWRkZGDnzp04fvw4nnrqKfTo0QN3794FAHz33XeYPXs2Pv74Yxw7dgweHh46AdjjZsyYgXnz5uGDDz7AuXPnsG7dOri5uQEoDdYA4JdffkF6ejq2bNkCAPjyyy8xc+ZMfPzxxzh//jxiYmLwwQcfYM2aNQCAvLw8vPjii2jZsiWOHz+OOXPmYMqUKZV+TsRiMZYuXYozZ85gzZo1+O233zBt2jStPPn5+fj444+xZs0a/P7771AoFBgyZIhm/+7duzF8+HBMnDgR586dw4oVK5CYmKgJZomolglERA3cqFGjhP79+2seHzlyRHBychJeeeUVQRAEYfbs2YKFhYWQmZmpyfPrr78KMplMKCws1CqrWbNmwooVKwRBEISQkBAhIiJCa3+nTp2Edu3a6T23QqEQLC0thS+//FJvPa9duyYAEE6ePKmV7unpKaxbt04r7aOPPhJCQkIEQRCEFStWCI6OjkJeXp5mf3x8vN6yHuXl5SUsWrSo3P3fffed4OTkpHm8evVqAYBw+PBhTdr58+cFAMKRI0cEQRCELl26CDExMVrlfP3114KHh4fmMQDh+++/L/e8RGQ8HKNHRCZhx44dsLW1hVKpRElJCfr374/PP/9cs9/LywsuLi6ax8ePH8eDBw/g5OSkVU5BQQH++ecfAMD58+cRERGhtT8kJAR79uzRW4fz58+jqKgIPXr0MLjed+7cQWpqKsaMGYM33nhDk65UKjXj/86fP4927drB2tpaqx6VtWfPHsTExODcuXNQKBRQKpUoLCxEXl4ebGxsAADm5uYIDg7WHOPv7w97e3ucP38eTz/9NI4fP46jR49qteCpVCoUFhYiPz9fq45EVPMY6BGRSejWrRvi4+NhYWGBRo0a6Uy2KAtkyqjVanh4eGDv3r06ZVV1iRErK6tKH6NWqwGUdt926tRJa5+ZmRkAQBCEKtXnUTdu3MDzzz+PiIgIfPTRR3B0dMTBgwcxZswYrS5uoHR5lMeVpanVanz44Yf4v//7P508Uqm02vUkosphoEdEJsHGxgbNmzc3OP9TTz2FjIwMmJubw9vbW2+egIAAHD58GCNHjtSkHT58uNwyW7RoASsrK/z6668YO3aszn6JRAKgtAWsjJubGxo3boyrV6/i1Vdf1Vtuq1at8PXXX6OgoEATTFZUD32OHTsGpVKJzz77DGJx6fDt7777TiefUqnEsWPH8PTTTwMALl68iJycHPj7+wMofd4uXrxYqeeaiGoOAz0iIj169uyJkJAQDBgwAPPmzUPLli1x69Yt7Ny5EwMGDEBwcDD++9//YtSoUQgODsYzzzyDb7/9FmfPnoWvr6/eMqVSKaZPn45p06ZBIpEgLCwMd+7cwdmzZzFmzBi4urrCysoKu3btQpMmTSCVSiGXyzFnzhxMnDgRMpkMffv2RVFREY4dO4Z79+5h0qRJGDZsGGbOnIkxY8bg/fffx/Xr17FgwYJKXW+zZs2gVCrx+eefo1+/fvj999+xfPlynXwWFhZ4++23sXTpUlhYWOCtt95C586dNYHfrFmz8OKLL8LT0xODBg2CWCzGX3/9hb///htz586t/AtBRNXCWbdERHqIRCLs3LkTXbt2xeuvvw4/Pz8MGTIE169f18ySHTx4MGbNmoXp06cjKCgIN27cwJtvvllhuR988AEmT56MWbNmISAgAIMHD0ZmZiaA0vFvS5cuxYoVK9CoUSP0798fADB27Fh89dVXSExMRJs2bfDss88iMTFRsxyLra0ttm/fjnPnzqFDhw6YOXMm5s2bV6nrbd++PRYuXIh58+ahdevW+PbbbxEbG6uTz9raGtOnT8ewYcMQEhICKysrbNiwQbO/d+/e2LFjB5KTk9GxY0d07twZCxcuhJeXV6XqQ0TGIRKMMbiDiIiIiP512KJHRERE1EAx0CMiIiJqoBjoERERETVQDPSIiIiIGigGekREREQNFAM9IiIiogaKgR4RERFRA8VAj4iIiKiBYqBHRERE1EAx0CMiIiJqoBjoERERETVQ/w8bHo8Hh/ga1AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Section 28\n",
    "cm_plot, classification_report, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true, y_pred, display_labels, labels, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "75c64dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 29\n",
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4aaa4209",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 31\n",
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "45aaca05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_ds_4_t_1_trn_100>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 32\n",
    "# Log the classification report as an artifact\n",
    "classification_report = (pd.DataFrame({k: v for k, v in classification_report.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report': wandb.Table(\n",
    "    dataframe=classification_report)})\n",
    "\n",
    "classification_report_artifact = wandb.Artifact(\n",
    "      f'classification_report_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact.new_file('classification_report.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6db7bbf2",
   "metadata": {},
   "source": [
    "## Evaluation on Second Evaluation Set\n",
    "In this section, we will evaluate the performance of our model on a second evaluation set (`second_eval_set`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8cbb7ff7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['ds_4__task_1_eval_set', 'ds_x__task_1_full_eval', 'ds_4__task_1_train_set_100'])\n"
     ]
    }
   ],
   "source": [
    "print(datasets.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_eval_set_name = f'ds_{dataset_eval}__task_{task}_full_eval'\n",
    "full_eval_df = datasets[second_eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == second_eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9cfc903c",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_eval_df = datasets[second_eval_set_name]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "303aeed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Load the second evaluation set (second_eval_set)\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions_2 = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7c218926",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 494/494 [05:11<00:00,  1.59it/s]\n"
     ]
    }
   ],
   "source": [
    "for messages in tqdm(full_eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions_2.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "789fce14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add predictions to df\n",
    "full_eval_df['prediction'] = predictions_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "616c4658",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_output_dir_2 = os.path.join(output_dir, 'predictions_2',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir_2, exist_ok=True)\n",
    "datasets[second_eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir_2, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ec8481ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get performance metrics\n",
    "y_true_2 = full_eval_df['completion_label']\n",
    "y_pred_2 = full_eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "296deb14",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_type_2 = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels_2 = task_to_display_labels[task][label_type]\n",
    "labels_2 = display_labels_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "83dbd847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.6146572104018913,\n",
      "                 'precision': 0.4659498207885305,\n",
      "                 'recall': 0.9027777777777778,\n",
      "                 'support': 144},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.7320261437908497,\n",
      "                       'precision': 0.8275862068965517,\n",
      "                       'recall': 0.65625,\n",
      "                       'support': 256},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.18867924528301885,\n",
      "                  'precision': 0.8333333333333334,\n",
      "                  'recall': 0.10638297872340426,\n",
      "                  'support': 94},\n",
      " 'accuracy': 0.6234817813765182,\n",
      " 'macro avg': {'f1-score': 0.5117875331585866,\n",
      "               'precision': 0.7089564536728052,\n",
      "               'recall': 0.5551369188337273,\n",
      "               'support': 494},\n",
      " 'weighted avg': {'f1-score': 0.5944234416294204,\n",
      "                  'precision': 0.7232635151667995,\n",
      "                  'recall': 0.6234817813765182,\n",
      "                  'support': 494}}\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_plot, classification_report_2, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true_2, y_pred_2, display_labels_2, labels_2, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "20289ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "09a5abfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix_2': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2399e29a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_2_ds_4_t_1_trn_100>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Log the classification report as an artifact\n",
    "classification_report_2 = (pd.DataFrame({k: v for k, v in classification_report_2.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report_2': wandb.Table(\n",
    "    dataframe=classification_report_2)})\n",
    "\n",
    "classification_report_artifact_2 = wandb.Artifact(\n",
    "      f'classification_report_2_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact_2.new_file('classification_report_2.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report_2))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b0aa2d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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